Collaborative phenotyping effort of European Drosophila melanogaster populations
Genomic analyses (Kapun et al. 2020) showed that there is longitudinal population structure, continent-wide sweeps, candidate genes for local climate adaptation by using 48 pooled population samples from 32 locations.
In 2017, DrosEU consortium has decided to make a collaborative phenotyping effort of European Drosophila melanogaster populations at the Groningen meeting.
The call was made with approximate numbers of populations, lines, etc. and under predefined criteria (finish within a year, reps, blocks, etc) and participating labs have indicated which traits they are willing to phenotype.
Nine sampling locations were chosen based on genomic data and cover a wide range of latitude (~20°) and longitude (~40°) across the continent (see table below). From each location, 15 to 20 isofemale lines were established in corresponding labs at the sampling location and the isofemale lines were centrally maintained by Élio Sucena at Instituto Gulbenkian de Ciência (IGC), Lisbon, Portugal. A total of 173 isofemale lines were used in this study.
| Country | Location | Latitude | Longitude | Altitude | Collector |
|---|---|---|---|---|---|
| Portugal | Recarei | 41.150 | -8.410 | 175 | Jorge Vieira |
| Spain | Gimenells (Lleida) | 41.618 | 0.620 | 173 | Josefa Gonzalez |
| Denmark | Karensminde | 55.945 | 10.213 | 15 | Mads Schou |
| Germany | Munich | 48.180 | 11.610 | 520 | Amanda Glaser-Schmitt |
| Austria | Mauternbach | 48.375 | 15.560 | 572 | Andrea Betancourt |
| Finland | Akaa | 61.100 | 23.520 | 88 | Maaria Kankare |
| Ukraine | Uman | 48.753 | 30.206 | 214 | Iryna Kozeretska |
| Turkey | Yesiloz | 40.231 | 32.260 | 680 | Banu Onder |
| Russia | Valday | 57.979 | 33.244 | 217 | Elena Pasyukova |
## Scale on map varies by more than 10%, scale bar may be inaccurate
Contributed labs are Abbott, Bergland, Billeter, Colinet, Flatt, Fricke, Gibert, Gonzalez, Grath, Hoedjes, Kozeretska, Mensch, Onder, Parsch, Pasyukova, Posnien, Ritchie, Schlötterer, Schmidt, Stamenkovic-Radak, Sucena, Tauber, Vieira, Wegener, Zwaan and detailed list below :
| Country | Lab | PI | Trait |
|---|---|---|---|
| Sweden | Lund | Abbott | Pigmentation |
| USA | Charlottesville | Bergland | Diapause |
| The Netherlands | Groningen | Billeter | Fecundity |
| France | Rennes | Colinet | Dry weight |
| Switzerland | Fribourg | Flatt | Diapause |
| Switzerland | Fribourg | Flatt | Lifespan |
| Germany | Muenster | Fricke | Fecundity |
| France | Lyon | Gibert | Development time |
| France | Lyon | Gibert | Pigmentation |
| France | Lyon | Gibert | Viability |
| Spain | Barcelona | Gonzalez | Cold-shock mortality |
| Spain | Barcelona | Gonzalez | Starvation resistance |
| Germany | Munich | Grath | Development time |
| Germany | Munich | Grath | Viability |
| The Netherlands | Lausanne | Hoedjes | Development time |
| The Netherlands | Lausanne | Hoedjes | Dry weight |
| The Netherlands | Lausanne | Hoedjes | Viability |
| Ukraine | Kyiv | Kozeretska | Cold-shock mortality |
| Ukraine | Kyiv | Kozeretska | Thorax length |
| Argentina | Buenos Aiers | Mensch | Chill-coma recovery time |
| Turkey | Ankara | Onder | Dry weight |
| Turkey | Ankara | Onder | Starvation resistance |
| Turkey | Ankara | Onder | Wing area |
| Germany | Munich | Parsch | Heat-shock mortality |
| Germany | Munich | Parsch | Lifespan |
| Russia | Moscow | Pasyukova | Lifespan |
| Russia | Moscow | Pasyukova | Starvation resistance |
| Germany | Göttingen | Posnien | Thorax length |
| Germany | Göttingen | Posnien | Wing area |
| UK | St Andrews | Ritchie | Thorax length |
| UK | St Andrews | Ritchie | Wing area |
| UK | St Andrews | Ritchie | Wing patterning |
| Austria | Vienna | Schlötterer | Diapause |
| USA | Philadelphia | Schmidt | Development time |
| USA | Philadelphia | Schmidt | Pigmentation |
| USA | Philadelphia | Schmidt | Thorax length |
| USA | Philadelphia | Schmidt | Time to pupation |
| USA | Philadelphia | Schmidt | Viability |
| Serbia | Belgrade | Stamenkovic-Radak | Development time |
| Serbia | Belgrade | Stamenkovic-Radak | Viability |
| Serbia | Belgrade | Stamenkovic-Radak | Wing area |
| Portugal | Lisbon | Sucena | Fly husbandry |
| Israel | Haifa | Tauber | Locomotor activity |
| Portugal | Porto | Vieira | Chill-coma recovery time |
| Portugal | Porto | Vieira | Cold-shock mortality |
| Portugal | Porto | Vieira | Heat-shock mortality |
| Germany | Würzburg | Wegener | Circadian eclosion timing |
| The Netherlands | Wageningen | Zwaan | Development time |
| The Netherlands | Wageningen | Zwaan | Viability |
Esra Durmaz (all), Envel Kerdaffrec (all), Katja Hoedjes (Via, DT, DW), Banu Onder (SR, WA), Marija Tanaskovic (WA), Chris Wegener (CET), Eran Tauber (LA), Rudolf Rohr (UniFr), coordination-team
wolbachia_pop_freqs # Wolbachia frequency by country and population abbreviation
## Country Population Wolbachia_Freq
## 1 Finland AK 100.00000
## 2 Portugal GI 66.66667
## 3 Denmark KA 85.00000
## 4 Austria MA 75.00000
## 5 Germany MU 94.73684
## 6 Spain RE 47.05882
## 7 Ukraine UM 69.23077
## 8 Russia VA 75.00000
## 9 Turkey YE 85.00000
Contributors and methods for experiments
3-5 day old adults (at least 25 pairs) are allowed to lay eggs en masse. Yeast is provided to stimulate egg laying (for at least 2 hours). Eggs are collected, and 40 are placed in each vial1. Viability is calculated per vial, as the percentage of individuals that emerged from the 40 eggs.
Please see below number of
Developmental time is scored as both the egg-to-pupa and egg-to-adult development time. Both were scored twice a day, when the chamber lights are turned on and two hours before they are turned off. In order to measure the egg-to-pupa developmental time, the spot where a pupa is found is marked with a permanent marker to keep track of which pupae have emerged in each day. The egg-to-adult developmental time is estimated by counting all emerged adults from the vial, and by sexing them.
At day 7 after emergence, flies are killed by snap freezing them in liquid nitrogen, by putting them at -20ºC or by putting them into an ethyl acetate solution and stored at -20ºC. Then they are sexed and placed into 96 wells plates, and placed in an oven set at 60-70 °C, for at least 3 days. At this point flies can be stored at room temperature using a protective cover. If this is the case, the day before measurements are made dry flies are again placed for 24h in the oven (60-70 °C) to ensure material is well dehydrated. Flies are then placed on a small piece of aluminium foil for direct weight measurement on microbalance (accuracy 1µg).
Five to seven days old flies are placed onto a double-sided sticky tape attached to a microscope slide and a picture of the thorax taken using a digital camera connected to a dissecting microscope. The same magnification and resolution is always used to increase reproducibility, and a scale bar inserted on each photo to allow transforming pixels into µm units. Thorax length is defined as the distance from the anterior margin of the thorax to the posterior tip of the scutellum and it is measured using the “Straight Line” in ImageJ/Fiji.
Both the left and right wings of five to seven days old flies (10 flies per sex per replicate) are removed and placed into a drop of Entellan®Merck, Hoyer’s Medium, sticked to a double-side sticky tape, or taped directly to the slide. Pictures of the wing preparations are taken using a digital camera connected to a dissecting microscope. The same magnification and resolution is always used to increase reproducibility. A scale bar is placed on each photo to allow transforming pixels into µm units. Manual measurements of wing length and wing area are performed using the “Straight Line” and “Polygon Selection” tools, respectively of ImageJ/Fiji (10.1371/journal.pone.0000007).
For each isofemale line, 10 males and 10 females are placed together in single-sex groups and allowed to mature for five days. Then, they are placed together (5-7 pairs), and mating interactions observed to ensure successful mating (at least 10 min copulation duration) to ensure that we have five successfully mated females. After a successful mating, males are discarded and females allowed to oviposit alone for 48 hours, moved to another vial, and allowed to oviposit for four days, and again moved to another vial and allowed to oviposit for two days to check that of egg-laying stopped. Vials are incubated until all offspring is born. Individuals are then frozen and the offspring counted.
Line level lifespan: Ten flies per sex/line are placed in each vial. The age at death will be scored when changing the food, at least three times a week. Five replicates are used.
Population level lifespan: Flies are kept in 1L demography cages (5 flies per line/sex for each population). The age at death will be scored when changing the food, at least three times a week. Ten replicates were performed.
Batches of 15-20 seven days old flies are placed for 18 hours in an empty vial immersed in an ice-water slurry box placed at a 4°C room for 18 hours. Then the vials are removed to a bench in a 25°C room and mortality scored 24 hours later.
Sexed flies are placed in an empty vial immersed in an ice-water slurry box placed at a 4°C room in the morning. Six hours later, flies are removed from the tube to individual wells of 24 well plates while being kept on ice. A timer is started once the plate is moved from the ice to a bench in a 25°C room. Each fly is checked by eye for recovery for a maximum of 60 minutes. Flies that are able to stand on their legs are considered recovered and the CCRT (in seconds) recorded.
Batches of 15-20 seven days old flies are placed in empty vials inside a 37ºC incubator and mortality checked for 7 hours every 30 minutes.
In order to induce diapause, two hour old ‘phenotypic virgins’ (pharate or melanized with meconium visible) female flies are exposed to 12°C and 10:14 light/dark hours for 3 weeks, using an incubator that allows temperature tracking. Vials are changed once per week. After three weeks under diapause conditions, flies are frozen at -80C until dissection. Both ovaries will be examined and classified according to the following simplified ‘classification’: 1) < stage 10: diapause; 2) stage 10-13: intermediate; 3) stage 14: non-diapause.
The eclosion rhythmicity has been measured in outcrossed Munich, Akaa, Gimenells, Uman, Mauternbach, Karensminde, Valday, Yesiloz and Recarei strains at:
LD14:10 @ 29°C, flies raised at 29°C
DD 29°C after LD14:10 entrainment, flies raised at 29°C
LD14:10 @ 18°C, flies raised at 18°C
DD 18°C after LD14:10 entrainment, flies raised at 18°C
The locomotor activity of the flies (males only) was measured using the DAM2 Drosophila monitors (Trikinetics Inc., Waltham, MA). Flies were 1-3 days old. Single flies were placed in glass tubes (10 cm × 0.5 cm) that were filled with 2 cm sugar/agar medium. The monitors were placed in light chambers driven by LED, in an incubator at 24°C, ~30% humidity. The flies were entrained to a light-dark cycle (LD 12:12) for 5d and then allowed to free-run for 10 d in constant darkness (DD). The activity data were processed into 30 min bins, and four different variables were analysed. These included the circadian period, and the phase, which were analyzed using the FFT NLLS algorithm available at the BioDare2 server (https://biodare2.ed.ac.uk/). The other two variables, level of activity and the nocturnal/diurnal ratio were analyzed by a custom-made R script.
Batches of 10 sexed flies are transferred to glass vials filled with 5 mL of 2% agar for starvation 3-7 days after eclosion. The age at death will be scored every 8 hours.
For each line, 10 females, 13-15 days old, either alive or stored in 95% ethanol, are air dried and placed on their left side, and pictures taken using a dissecting microscope. Images are then analysed in ImageJ 1.46r, using the Area Fraction measurement in the Analyze menu. Area Fraction measures the percentage of pixels in a selected area that have been highlighted in red using the Threshold tool. This gives an estimate of the percentage of dark pigmentation on the three terminal tergites of the abdomen (4, 5 and 6).
MasterSheets
All data files are reformatted as so called Master Sheets , in which the data structure is the same for all the sub-data sets and all details/edits logged in a README sheet.
| Headers | Descriptions |
|---|---|
| Supervisor/PI | Lab identifier |
| Diet | Either standardized S or non-standardized NS |
| Batch | From 1 to n |
| Population | Population identifier in abbreviated form (i.e. AK) |
| Line | Line identifier (i.e. MU1) |
| Sex | Either female (F) or male (M) |
| ReplicateVial | Replicate identifier (from 1 to n) |
| TraitValue | Trait value in x units |
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
For each trait, sex and lab, we run counts for population, line and replicate vial (if applicable).
insert count table
text BLA BLA BLA
Here is an example code for tables (egg-to-adult viability). The very same code is used for other traits (either with or without Batch) by filtering the data at supervisor, batch, population and/or line levels (and also for sex, if applicable).
## First defined standard error and coefficient of variation
std_err <- function(x) sd(x)/sqrt(length(x))
coef_var <- function(x) sd(x)/mean(x)
estimate_mode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
# Then compute descriptive statistics (mean, median, minimum, maximum, standard error, coefficient of variation and mode) at the defined levels (line with batch information example below) :
table_Via_Line_wbatch <- write.csv(d_Via %>% group_by(Supervisor.PI, Batch, Population, Line) %>% summarise_at(vars(ProportionEggtoAdultSurvival), list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, SE = std_err, CV = coef_var, Mode = estimate_mode)), file = "Viability/table_Via_Line_wbatch.csv", row.names = T)
table_Via_Line_wbatch <- read.csv("Viability/table_Via_Line_wbatch.csv") #file name is here
Tables for descriptive statistics at population and line levels can be found in the trait directory, under the file name table_TraitAbbreviation_Level_BatchInfo.csv (i.e. Viability/table_Via_Line_wobatch.csv)
Data range is calculated with #r min() and #r max() functions for each trait.
y-axis on the figures is scaled by the minimum (#r min_trait) and maximum (#r max_trait) values in the full data set for a given trait.
The very same ggplot theme has been used all across the document, called droseu_theme
droseu_theme <- theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black",), axis.title.x = element_text(size = 16), axis.text.x = element_text(size = 16),axis.text.y = element_text(size = 16),axis.title.y = element_text(size = 16))
p_TraitAbbreviation_Lab + ylim(c(min_trait, max_triat)) + droseu_theme
Here is an example code for figures (egg-to-adult viabiliy, Gibert Lab). The very same code is used to generate plots for other contributing labs and traits (either for Batch or Population) by filtering the data at supervisor level (for females and males separately, if applicable).
p_Via_Gibert <- ggplot(data = (subset(d_Via,Supervisor.PI=='Gibert')), #subsets for supervisor
aes(x=Population_Lat, y=ProportionEggtoAdultSurvival, fill=Batch)) + #define x- and y-axis
geom_boxplot(outlier.shape = NA, notch=FALSE) + #boxplot
labs(title="p_Via_Gibert", x="Population", y = "ProportionEggtoAdultSurvival") +
ylim(c(min_Via, max_Via)) +
droseu_theme #y-axis limits, axis labels and droseu_theme
pdf(file="Viability/p_Via_Gibert.pdf",width=8, height=5)
p_Via_Gibert
invisible(dev.off())
Here is an example code for linear models (egg-to-adult development time, males, Gibert Lab). The very same code is used for other contributing labs and traits by filtering the data at supervisor level (for females and males separately, if applicable), adding or removing factors if they cause convergence problems or singularity (i.e. if only one replicate vial has been phenotyped per line and/or population and/or batch, the lowest level [ replicate_vial in this examplstae] has been dropped from given model).
DT_A_M_lmer_Gibert <- lmer(DT_EggAdult ~ Population + (1|Line:Population) + #Line (random) is nested in Population (fixed)
(1|Batch) + #Batch is a random effect
(1|ReplicateVial : Line), #Replicate vial (random) is nested in Line (random)
data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Gibert",]) #Filter by Lab in development time, males data
capture.output(summary(DT_A_M_lmer_Gibert),file = "DevelopmentTime/DT_A_M_lmer_Gibert_sum.txt") #save output summary
capture.output(anova(DT_A_M_lmer_Gibert),file = "DevelopmentTime/DT_A_M_lmer_Gibert.txt") #save anova output
capture.output(emmeans(DT_A_M_lmer_Gibert, list(pairwise ~ Population), adjust = "tukey", mode = "asymp"),file = "DevelopmentTime/DT_A_M_lmer_Gibert_tk.txt") #run and save post-hoc test (tukey) output
anova(DT_A_M_lmer_Gibert) #print anova output
summary(DT_A_M_lmer_Gibert) #print model output summary
Here is an example code for linear models for geography (altitude, dry weight, males, Onder Lab). The very same code is used for other contributing labs and traits by filtering the data at supervisor level (for females and males separately, if applicable), adding or removing factors if they cause convergence problems or singularity (i.e. if only one replicate vial has been phenotyped per line and/or population and/or batch, the lowest level [ replicate_vial in this example] has been dropped from given model) and also for latitude and longitude.
DW_M_lmer_Alt_Onder <- lmer(DW_micrograms ~ Altitude + (1|Population) + (1|Population:Line) + (1|Batch), data = d_DW_M[d_DW_M$Supervisor.PI == "Onder",])
capture.output(summary(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder_sum.txt")
capture.output(anova(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder.txt")
Here is an example code for survival analyses (starvation resistance, females, Gonzalez Lab). The very same code is used for other contributing labs and traits by filtering the data at supervisor level and sex. Please note that the level “replicate_vial” has been dropped from all survival analyses, as it clogs the models due to the volume of data.
SR_F_coxme_Gonzalez <- coxme(Surv(AgeAtDeath_hours, Censor) ~ Population + (1|Batch) + (1|Population/Line) , data = filter(d_SR_surv, Supervisor.PI == "Gonzalez", Sex == "F"))
capture.output(summary(SR_F_coxme_Gonzalez), file = "Starvation/SR_F_coxme_Gonzalez_sum.txt")
capture.output(anova(SR_F_coxme_Gonzalez), file = "Starvation/SR_F_coxme_Gonzalez.txt")
Analyses outputs can be found in the trait directory, under the file name TraitAbbreviation_Sex_Function_LabIdentifier.txt (i.e. DevelopmentTime/DT_A_M_lmer_Gibert.txt)
Please note that “Plots and Linear Models by Lab” are presented in alphabetical order.
For a detailed description of tables, plots, linear models and outputs, please refer here
Gibert Lab :Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Patricia Gibert
Grath Lab : Ingo Müller, Sonja Grath
Hoedjes Lab : Hristina Kostic, Katja Hoedjes
Schmidt Lab : Ozan Kiratli, Yonatan Babore, Liam Forsythe, Paul Schmidt
Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak
Zwaan Lab : Joost van den Heuvel, Bas Zwaan
Reading data in R
d_Via <- read.csv("MasterSheets_Oct21_git/VIA_MasterSheet_Oct21.csv")
str(d_Via)
## 'data.frame': 2367 obs. of 12 variables:
## $ Supervisor.PI : chr "Gibert" "Gibert" "Gibert" "Gibert" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK10" ...
## $ ReplicateVialOld : int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : chr "Gibert_1_AK1_1" "Gibert_1_AK1_2" "Gibert_1_AK1_3" "Gibert_1_AK10_1" ...
## $ ProportionEggtoAdultSurvival: num 0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
Factors need reformatting (i.e. Supervisor.PI should be coded as a factor, not character).
str(d_Via)
## 'data.frame': 2367 obs. of 12 variables:
## $ Supervisor.PI : chr "Gibert" "Gibert" "Gibert" "Gibert" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK10" ...
## $ ReplicateVialOld : int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : chr "Gibert_1_AK1_1" "Gibert_1_AK1_2" "Gibert_1_AK1_3" "Gibert_1_AK10_1" ...
## $ ProportionEggtoAdultSurvival: num 0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_Via$Supervisor.PI <- as.factor(d_Via$Supervisor.PI)
d_Via$Diet <- as.factor(d_Via$Diet)
d_Via$Batch <- as.factor(d_Via$Batch)
d_Via$Population_Lat <- factor(d_Via$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Via$Population_Lon <- factor(d_Via$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Via$Population_Alt <- factor(d_Via$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Via$Line <- as.factor(d_Via$Line)
d_Via$ReplicateVial <- as.factor(d_Via$ReplicateVial)
d_Via$ProportionEggtoAdultSurvival <- as.numeric(d_Via$ProportionEggtoAdultSurvival)
d_Via$Country <- as.factor(d_Via$Country)
d_Via$Latitude <- as.numeric(d_Via$Latitude)
d_Via$Longitude <- as.numeric(d_Via$Longitude)
d_Via$Altitude <- as.numeric(d_Via$Altitude)
# Now they should be in the correct format, see below.
str(d_Via)
## 'data.frame': 2367 obs. of 15 variables:
## $ Supervisor.PI : Factor w/ 6 levels "Gibert","Grath",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 172 levels "AK1","AK10","AK11",..: 1 1 1 2 2 2 4 4 4 9 ...
## $ ReplicateVialOld : int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : Factor w/ 2367 levels "Gibert_1_AK1_1",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ ProportionEggtoAdultSurvival: num 0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
## $ Country : Factor w/ 9 levels "Austria","Denmark",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : num 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
# Voila!
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_Via <- min(d_Via$ProportionEggtoAdultSurvival)
max_Via <- max(d_Via$ProportionEggtoAdultSurvival)
y-axis is scaled by the minimum (0) and maximum (1) values in the full data set.
anova(Via_lmer_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.80451 0.10056 8 153.66 8.0873 4.389e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Gibert)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +
## (1 | Batch)
## Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Gibert", ]
##
## REML criterion at convergence: -547.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1956 -0.5255 -0.0195 0.5213 2.8588
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.012426 0.11147
## Batch (Intercept) 0.000125 0.01118
## Residual 0.012435 0.11151
## Number of obs: 532, groups: Line:Population, 169; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.06673 0.02957 27.03333 36.074 < 2e-16 ***
## PopulationGI -0.16344 0.04382 154.67733 -3.730 0.000269 ***
## PopulationKA -0.04174 0.04065 155.52140 -1.027 0.306077
## PopulationMA -0.12876 0.04058 153.42366 -3.173 0.001822 **
## PopulationMU -0.04274 0.04059 154.79040 -1.053 0.293955
## PopulationRE -0.12273 0.04311 155.47126 -2.847 0.005017 **
## PopulationUM -0.05047 0.04169 154.54796 -1.211 0.227874
## PopulationVA -0.14130 0.04059 154.79040 -3.481 0.000649 ***
## PopulationYE -0.26929 0.04059 154.79040 -6.634 5.16e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.631
## PopulatinKA -0.681 0.463
## PopulatinMA -0.684 0.463 0.500
## PopulatinMU -0.682 0.463 0.500 0.500
## PopulatinRE -0.642 0.437 0.470 0.470 0.471
## PopulatinUM -0.664 0.451 0.487 0.487 0.487 0.458
## PopulatinVA -0.682 0.463 0.500 0.500 0.501 0.471 0.487
## PopulatinYE -0.682 0.463 0.500 0.500 0.501 0.471 0.487 0.501
anova(Via_lmer_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.077956 0.038978 2 27.308 1.9446 0.1624
summary(Via_lmer_Grath)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population)
## Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Grath", ]
##
## REML criterion at convergence: -123.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1670 -0.5714 0.0117 0.4894 2.9754
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.004157 0.06447
## Residual 0.020045 0.14158
## Number of obs: 147, groups: Line:Population, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.98774 0.02906 28.44269 33.995 <2e-16 ***
## PopulationMU -0.07674 0.04075 27.59627 -1.883 0.0703 .
## PopulationRE -0.05958 0.04075 27.59627 -1.462 0.1551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltMU
## PopulatinMU -0.713
## PopulatinRE -0.713 0.508
anova(Via_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.51582 0.064478 8 158 6.2599 4.985e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +
## (1 | Batch)
## Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Hoedjes", ]
##
## REML criterion at convergence: -549.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3516 -0.5255 -0.0429 0.4980 4.2544
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 1.545e-02 1.243e-01
## Batch (Intercept) 4.088e-12 2.022e-06
## Residual 1.030e-02 1.015e-01
## Number of obs: 501, groups: Line:Population, 167; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.09520 0.03073 157.99981 35.644 < 2e-16 ***
## PopulationGI -0.20137 0.04693 157.99994 -4.290 3.10e-05 ***
## PopulationKA -0.06612 0.04345 157.99994 -1.522 0.130106
## PopulationMA -0.08792 0.04345 157.99994 -2.023 0.044720 *
## PopulationMU -0.04924 0.04345 157.99994 -1.133 0.258863
## PopulationRE -0.17978 0.04693 157.99994 -3.831 0.000184 ***
## PopulationUM -0.10841 0.04533 157.99994 -2.392 0.017952 *
## PopulationVA -0.11483 0.04345 157.99994 -2.643 0.009052 **
## PopulationYE -0.24862 0.04345 157.99994 -5.722 5.15e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655
## PopulatinKA -0.707 0.463
## PopulatinMA -0.707 0.463 0.500
## PopulatinMU -0.707 0.463 0.500 0.500
## PopulatinRE -0.655 0.429 0.463 0.463 0.463
## PopulatinUM -0.678 0.444 0.479 0.479 0.479 0.444
## PopulatinVA -0.707 0.463 0.500 0.500 0.500 0.463 0.479
## PopulatinYE -0.707 0.463 0.500 0.500 0.500 0.463 0.479 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
anova(Via_lm_Schmidt) #lm()` is used only for Schmidt Lab's viability data, as only one vial per line was phenotyped
## Analysis of Variance Table
##
## Response: ProportionEggtoAdultSurvival
## Df Sum Sq Mean Sq F value Pr(>F)
## Population 8 1.7653 0.220666 2.6999 0.008308 **
## Residuals 153 12.5050 0.081732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lm_Schmidt)
##
## Call:
## lm(formula = ProportionEggtoAdultSurvival ~ Population, data = d_Via_trans[d_Via_trans$Supervisor.PI ==
## "Schmidt", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73241 -0.21504 0.01621 0.15661 0.83839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.92028 0.06393 14.396 <2e-16 ***
## PopulationGI -0.18787 0.09765 -1.924 0.0562 .
## PopulationKA 0.21332 0.09041 2.360 0.0196 *
## PopulationMA 0.01281 0.09288 0.138 0.8905
## PopulationMU -0.03180 0.09041 -0.352 0.7255
## PopulationRE 0.02327 0.09765 0.238 0.8120
## PopulationUM 0.10329 0.09962 1.037 0.3015
## PopulationVA -0.03068 0.09041 -0.339 0.7348
## PopulationYE -0.08020 0.09041 -0.887 0.3764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2859 on 153 degrees of freedom
## Multiple R-squared: 0.1237, Adjusted R-squared: 0.07789
## F-statistic: 2.7 on 8 and 153 DF, p-value: 0.008308
p_Via_pop_Schmidt
anova(Via_lmer_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.50121 0.062652 8 155.29 5.2001 9.104e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_StamenkovicRadak)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +
## (1 | Batch)
## Data: d_Via_trans[d_Via_trans$Supervisor.PI == "StamenkovicRadak", ]
##
## REML criterion at convergence: -485.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0263 -0.5091 -0.0167 0.5112 3.1782
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.015859 0.1259
## Batch (Intercept) 0.001318 0.0363
## Residual 0.012048 0.1098
## Number of obs: 501, groups: Line:Population, 167; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.98823 0.03644 24.11360 27.117 < 2e-16 ***
## PopulationGI -0.09533 0.05031 155.41420 -1.895 0.05998 .
## PopulationKA -0.04514 0.04464 155.26320 -1.011 0.31344
## PopulationMA -0.14459 0.04460 155.11506 -3.242 0.00145 **
## PopulationMU 0.01274 0.04460 155.09434 0.286 0.77548
## PopulationRE -0.07059 0.04737 155.37260 -1.490 0.13819
## PopulationUM -0.10798 0.04589 155.37865 -2.353 0.01987 *
## PopulationVA -0.13926 0.04460 155.11479 -3.122 0.00214 **
## PopulationYE -0.21115 0.04464 155.26320 -4.730 5e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.542
## PopulatinKA -0.614 0.443
## PopulatinMA -0.611 0.442 0.499
## PopulatinMU -0.613 0.442 0.500 0.500
## PopulatinRE -0.579 0.415 0.472 0.471 0.472
## PopulatinUM -0.598 0.429 0.488 0.486 0.487 0.460
## PopulatinVA -0.612 0.445 0.500 0.499 0.500 0.470 0.485
## PopulatinYE -0.614 0.443 0.501 0.499 0.500 0.472 0.488 0.500
anova(Via_lmer_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1.6727 0.20908 8 150.05 6.3093 4.877e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Zwaan)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Batch) + (1 |
## Line:Population)
## Data: d_Via_trans[d_Via_trans$Supervisor.PI == "Zwaan", ]
##
## REML criterion at convergence: -124.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5670 -0.4679 0.0236 0.4732 2.8902
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.01407 0.1186
## Batch (Intercept) 0.00000 0.0000
## Residual 0.03314 0.1820
## Number of obs: 524, groups: Line:Population, 169; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.072023 0.035162 146.338135 30.488 < 2e-16 ***
## PopulationGI -0.134116 0.053744 145.530211 -2.495 0.0137 *
## PopulationKA 0.006813 0.049907 147.722344 0.137 0.8916
## PopulationMA -0.045478 0.049950 148.488248 -0.910 0.3640
## PopulationMU -0.053131 0.050371 151.671972 -1.055 0.2932
## PopulationRE -0.122316 0.052526 144.039216 -2.329 0.0213 *
## PopulationUM 0.033358 0.051199 147.170696 0.652 0.5157
## PopulationVA -0.019751 0.050311 151.428483 -0.393 0.6952
## PopulationYE -0.258184 0.050259 151.942216 -5.137 8.44e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.654
## PopulatinKA -0.705 0.461
## PopulatinMA -0.704 0.461 0.496
## PopulatinMU -0.698 0.457 0.492 0.491
## PopulatinRE -0.669 0.438 0.472 0.471 0.467
## PopulatinUM -0.687 0.449 0.484 0.483 0.479 0.460
## PopulatinVA -0.699 0.457 0.492 0.492 0.488 0.468 0.480
## PopulatinYE -0.700 0.458 0.493 0.492 0.488 0.468 0.480 0.489
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#could drop batch, as it explains nothing
There are 3 populations and 29 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Schmidt Lab : Paul Schmidt
Reading data in R
d_DT_P <- read.csv("MasterSheets_Oct21_git/DTP_MasterSheet_Oct21.csv")
str(d_DT_P) #Factors need reformatting
## 'data.frame': 3391 obs. of 13 variables:
## $ Supervisor.PI : chr "Schmidt" "Schmidt" "Schmidt" "Schmidt" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "VA" "VA" "VA" "VA" ...
## $ Line : chr "VA35" "VA35" "VA35" "VA35" ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : chr "Schmidt_1_VA35_1" "Schmidt_1_VA35_1" "Schmidt_1_VA35_1" "Schmidt_1_VA35_1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DT_EggPupa : int 120 120 120 120 136 136 136 136 136 136 ...
## $ Country : chr "Russia" "Russia" "Russia" "Russia" ...
## $ Latitude : num 58 58 58 58 58 ...
## $ Longitude : num 33.2 33.2 33.2 33.2 33.2 ...
## $ Altitude : int 217 217 217 217 217 217 217 217 217 217 ...
d_DT_P$Supervisor.PI <- as.factor(d_DT_P$Supervisor.PI)
d_DT_P$Diet <- as.factor(d_DT_P$Diet)
d_DT_P$Batch <- as.factor(d_DT_P$Batch)
d_DT_P$Population_Lat <- factor(d_DT_P$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_DT_P$Population_Lon <- factor(d_DT_P$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_DT_P$Population_Alt <- factor(d_DT_P$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_DT_P$Line <- as.factor(d_DT_P$Line)
d_DT_P$ReplicateVial <- as.factor(d_DT_P$ReplicateVial)
d_DT_P$DT_EggPupa <- as.numeric(d_DT_P$DT_EggPupa)
str(d_DT_P) #They should be ok now.
## 'data.frame': 3391 obs. of 16 variables:
## $ Supervisor.PI : Factor w/ 1 level "Schmidt": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "VA" "VA" "VA" "VA" ...
## $ Line : Factor w/ 161 levels "AK1","AK10","AK11",..: 131 131 131 131 131 131 131 131 131 131 ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : Factor w/ 161 levels "Schmidt_1_AK1_1",..: 131 131 131 131 131 131 131 131 131 131 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DT_EggPupa : num 120 120 120 120 136 136 136 136 136 136 ...
## $ Country : chr "Russia" "Russia" "Russia" "Russia" ...
## $ Latitude : num 58 58 58 58 58 ...
## $ Longitude : num 33.2 33.2 33.2 33.2 33.2 ...
## $ Altitude : int 217 217 217 217 217 217 217 217 217 217 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 8 8 8 8 8 8 8 8 8 8 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 6 6 6 6 6 6 6 6 6 6 ...
# Note that the trait has been phenotyped only in Schmidt lab and in one batch.
Descriptive statistics at the line level :
Descriptive statistics at the population level:
min_DT_P <- min(d_DT_P$DT_EggPupa)
max_DT_P <- max(d_DT_P$DT_EggPupa)
y-axis is scaled by the minimum (96) and maximum (192) values in the full data set.
anova(DT_P_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 2515.1 314.38 8 147.95 2.6412 0.009804 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_P_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggPupa ~ Population + (1 | Population:Line)
## Data: d_DT_P
##
## REML criterion at convergence: 26303.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9734 -0.6092 -0.0831 0.3912 4.7796
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 158.4 12.59
## Residual 119.0 10.91
## Number of obs: 3391, groups: Population:Line, 161
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 127.5020 2.8668 143.5783 44.475 < 2e-16 ***
## PopulationGI 12.9203 4.7140 157.7547 2.741 0.00684 **
## PopulationKA -0.2098 4.0656 144.7480 -0.052 0.95892
## PopulationMA 4.5016 4.1179 144.9709 1.093 0.27613
## PopulationMU 2.2279 4.0527 143.3696 0.550 0.58337
## PopulationRE 7.7677 4.4068 146.8919 1.763 0.08004 .
## PopulationUM 3.6588 4.3705 142.4818 0.837 0.40391
## PopulationVA 13.2633 4.1248 145.8807 3.215 0.00160 **
## PopulationYE 7.6982 4.0711 145.8355 1.891 0.06062 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.608
## PopulatinKA -0.705 0.429
## PopulatinMA -0.696 0.423 0.491
## PopulatinMU -0.707 0.430 0.499 0.492
## PopulatinRE -0.651 0.396 0.459 0.453 0.460
## PopulatinUM -0.656 0.399 0.463 0.457 0.464 0.427
## PopulatinVA -0.695 0.423 0.490 0.484 0.492 0.452 0.456
## PopulatinYE -0.704 0.428 0.497 0.490 0.498 0.458 0.462 0.489
p_DT_P_Schmidt
For a detailed description of tables, plots, linear models and outputs, please refer here
Gibert Lab : Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Patricia Gibert
Grath Lab : Ingo Müller, Sonja Grath
Hoedjes Lab : Hristina Kostic, Katja Hoedjes
Schmidt Lab : Ozan Kiratli, Yonatan Babore, Liam Forsythe, Paul Schmidt
Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak
Zwaan Lab : Joost van den Heuvel, Bas Zwaan
d_DT_A <- read.csv("MasterSheets_Oct21_git/DTA_MasterSheet_Oct21.csv")
str(d_DT_A) #Factors need reformatting
## 'data.frame': 57609 obs. of 14 variables:
## $ Supervisor.PI : chr "Gibert" "Gibert" "Gibert" "Gibert" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : chr "Gibert_1_AK1_1" "Gibert_1_AK1_1" "Gibert_1_AK1_1" "Gibert_1_AK1_1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DT_EggAdult : num 202 202 202 202 202 202 202 202 202 202 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_DT_A$Supervisor.PI <- as.factor(d_DT_A$Supervisor.PI)
d_DT_A$Diet <- as.factor(d_DT_A$Diet)
d_DT_A$Batch <- as.factor(d_DT_A$Batch)
d_DT_A$Population_Lat <- factor(d_DT_A$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_DT_A$Population_Lon <- factor(d_DT_A$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_DT_A$Population_Alt <- factor(d_DT_A$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_DT_A$Line <- as.factor(d_DT_A$Line)
d_DT_A$Sex <- as.factor(d_DT_A$Sex)
d_DT_A$ReplicateVial <- as.factor(d_DT_A$ReplicateVial)
d_DT_A$DT_EggAdult <- as.numeric(d_DT_A$DT_EggAdult)
str(d_DT_A) #They should be ok now.
## 'data.frame': 57609 obs. of 17 variables:
## $ Supervisor.PI : Factor w/ 6 levels "Gibert","Grath",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : Factor w/ 2300 levels "Gibert_1_AK1_1",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DT_EggAdult : num 202 202 202 202 202 202 202 202 202 202 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
#Create data sheets for females (d_DT_A_F) and males (d_DT_A_M), by sorting at sex level.
d_DT_A_F <-subset(d_DT_A,Sex=='F')
d_DT_A_M <-subset(d_DT_A,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_DT_A <- min(d_DT_A$DT_EggAdult)
max_DT_A <- max(d_DT_A$DT_EggAdult)
y-axis is scaled by the minimum (150) and maximum (394) values in the full data set.
anova(DT_A_F_lmer_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 2839.4 354.93 8 157.74 3.7809 0.0004399 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Gibert)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Gibert", ]
##
## REML criterion at convergence: 50925
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4211 -0.5914 -0.1334 0.4381 5.9468
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 4.727 2.174
## Line:Population (Intercept) 36.110 6.009
## Batch (Intercept) 1.175 1.084
## Residual 93.874 9.689
## Number of obs: 6817, groups:
## ReplicateVial:Line, 531; Line:Population, 169; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 222.3797 1.5710 19.3362 141.556 <2e-16 ***
## PopulationGI 4.3367 2.1688 156.6501 2.000 0.0473 *
## PopulationKA 0.9782 2.0009 154.4950 0.489 0.6256
## PopulationMA -0.2899 2.0046 155.4376 -0.145 0.8852
## PopulationMU 1.3078 2.0003 154.3141 0.654 0.5142
## PopulationRE 5.4719 2.1366 158.5080 2.561 0.0114 *
## PopulationUM 3.1059 2.0547 154.2259 1.512 0.1327
## PopulationVA 1.5098 2.0056 155.9084 0.753 0.4527
## PopulationYE -4.5778 2.0223 160.6684 -2.264 0.0249 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.572
## PopulatinKA -0.620 0.461
## PopulatinMA -0.619 0.460 0.500
## PopulatinMU -0.621 0.461 0.501 0.500
## PopulatinRE -0.581 0.433 0.469 0.467 0.469
## PopulatinUM -0.604 0.449 0.488 0.487 0.488 0.456
## PopulatinVA -0.619 0.460 0.500 0.499 0.500 0.467 0.487
## PopulatinYE -0.614 0.456 0.496 0.495 0.496 0.463 0.483 0.494
anova(DT_A_M_lmer_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 2526.4 315.8 8 155.05 2.9617 0.004119 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Gibert)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Gibert", ]
##
## REML criterion at convergence: 49809.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4032 -0.6057 -0.1597 0.4187 5.0802
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 3.6928 1.9217
## Line:Population (Intercept) 31.0297 5.5704
## Batch (Intercept) 0.6211 0.7881
## Residual 106.6292 10.3261
## Number of obs: 6566, groups:
## ReplicateVial:Line, 533; Line:Population, 169; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 226.5155 1.4120 30.2571 160.427 < 2e-16 ***
## PopulationGI 5.3606 2.0277 153.6251 2.644 0.00905 **
## PopulationKA 0.7077 1.8726 152.1759 0.378 0.70601
## PopulationMA -1.2153 1.8789 154.0130 -0.647 0.51870
## PopulationMU 1.2857 1.8708 151.6666 0.687 0.49300
## PopulationRE 2.0681 1.9870 152.3834 1.041 0.29961
## PopulationUM 3.2574 1.9227 151.8288 1.694 0.09227 .
## PopulationVA 1.8677 1.8759 153.1712 0.996 0.32100
## PopulationYE -2.9951 1.8932 158.5817 -1.582 0.11564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.600
## PopulatinKA -0.650 0.460
## PopulatinMA -0.648 0.458 0.497
## PopulatinMU -0.650 0.460 0.499 0.498
## PopulatinRE -0.612 0.434 0.469 0.468 0.470
## PopulatinUM -0.633 0.448 0.486 0.484 0.486 0.457
## PopulatinVA -0.649 0.459 0.498 0.496 0.498 0.469 0.485
## PopulatinYE -0.643 0.455 0.493 0.492 0.494 0.464 0.480 0.492
anova(DT_A_F_lmer_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 5055.6 2527.8 2 16.067 8.0465 0.003792 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Grath)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
## Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Grath", ]
##
## REML criterion at convergence: 7659.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0678 -0.7463 0.0894 0.5972 4.2158
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 22.4 4.732
## Residual 314.1 17.724
## Number of obs: 890, groups: Line:Population, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 309.626 1.722 13.465 179.800 < 2e-16 ***
## PopulationMU -3.995 2.459 13.988 -1.624 0.12660
## PopulationRE 11.990 3.989 18.136 3.006 0.00755 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltMU
## PopulatinMU -0.700
## PopulatinRE -0.432 0.302
anova(DT_A_M_lmer_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1999.4 999.71 2 18.482 3.3225 0.05852 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Grath)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
## Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Grath", ]
##
## REML criterion at convergence: 7557.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4732 -0.5678 -0.0897 0.6034 4.2694
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 47.61 6.90
## Residual 300.89 17.35
## Number of obs: 881, groups: Line:Population, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 317.567 2.349 17.582 135.217 <2e-16 ***
## PopulationMU -5.420 3.330 17.762 -1.628 0.121
## PopulationRE 6.730 4.998 19.040 1.347 0.194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltMU
## PopulatinMU -0.705
## PopulatinRE -0.470 0.331
anova(DT_A_F_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 6899 862.38 8 155.01 5.6745 2.514e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Hoedjes", ]
##
## REML criterion at convergence: 54971.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7059 -0.5552 -0.1930 0.3779 12.7479
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 4.685 2.164
## Line:Population (Intercept) 21.642 4.652
## Batch (Intercept) 1.381 1.175
## Residual 151.975 12.328
## Number of obs: 6939, groups:
## ReplicateVial:Line, 501; Line:Population, 167; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 231.2781 1.2896 30.0406 179.348 < 2e-16 ***
## PopulationGI 6.6655 1.7839 155.6038 3.737 0.000261 ***
## PopulationKA -0.1798 1.6299 148.2949 -0.110 0.912318
## PopulationMA 1.9330 1.6342 149.8397 1.183 0.238743
## PopulationMU -1.7771 1.6284 147.7808 -1.091 0.276922
## PopulationRE 6.4500 1.7845 154.0927 3.614 0.000407 ***
## PopulationUM 0.9172 1.7105 151.0393 0.536 0.592592
## PopulationVA -1.3977 1.6366 150.5099 -0.854 0.394424
## PopulationYE 1.8985 1.6491 154.8806 1.151 0.251422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.573
## PopulatinKA -0.627 0.453
## PopulatinMA -0.625 0.452 0.495
## PopulatinMU -0.628 0.454 0.497 0.495
## PopulatinRE -0.573 0.420 0.453 0.452 0.454
## PopulatinUM -0.597 0.435 0.473 0.471 0.473 0.439
## PopulatinVA -0.624 0.451 0.494 0.493 0.494 0.451 0.471
## PopulatinYE -0.620 0.448 0.490 0.489 0.491 0.448 0.467 0.488
anova(DT_A_M_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 9369.8 1171.2 8 153.78 5.7073 2.332e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Hoedjes", ]
##
## REML criterion at convergence: 54596.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.8700 -0.5946 -0.0822 0.3611 10.9394
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 3.107 1.763
## Line:Population (Intercept) 24.219 4.921
## Batch (Intercept) 2.565 1.602
## Residual 205.215 14.325
## Number of obs: 6649, groups:
## ReplicateVial:Line, 501; Line:Population, 167; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 238.6853 1.4661 19.7755 162.803 < 2e-16 ***
## PopulationGI 5.9614 1.8999 154.5025 3.138 0.00204 **
## PopulationKA -2.5325 1.7402 149.2985 -1.455 0.14768
## PopulationMA 0.6822 1.7356 147.7901 0.393 0.69486
## PopulationMU -2.1539 1.7370 148.2171 -1.240 0.21695
## PopulationRE 5.7531 1.9095 155.4192 3.013 0.00302 **
## PopulationUM -0.9229 1.8237 150.9070 -0.506 0.61354
## PopulationVA -2.8591 1.7426 149.9496 -1.641 0.10294
## PopulationYE 0.5190 1.7673 157.2861 0.294 0.76941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.542
## PopulatinKA -0.591 0.456
## PopulatinMA -0.593 0.458 0.499
## PopulatinMU -0.592 0.457 0.499 0.500
## PopulatinRE -0.539 0.423 0.454 0.455 0.455
## PopulatinUM -0.564 0.439 0.475 0.477 0.476 0.442
## PopulatinVA -0.590 0.456 0.497 0.499 0.498 0.454 0.475
## PopulatinYE -0.582 0.449 0.490 0.492 0.491 0.446 0.468 0.490
anova(DT_A_F_lmer_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 5630 703.75 8 145.04 3.2466 0.001981 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Schmidt)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
## Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Schmidt", ]
##
## REML criterion at convergence: 14067.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5685 -0.4672 -0.0841 0.3244 9.1996
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 382.1 19.55
## Residual 216.8 14.72
## Number of obs: 1664, groups: Line:Population, 159
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 218.139 4.503 141.010 48.438 < 2e-16 ***
## PopulationGI 8.468 7.686 149.902 1.102 0.2724
## PopulationKA 1.889 6.453 140.874 0.293 0.7702
## PopulationMA 6.233 6.472 142.375 0.963 0.3371
## PopulationMU 3.018 6.360 140.236 0.475 0.6358
## PopulationRE 11.911 6.954 145.726 1.713 0.0889 .
## PopulationUM 4.043 6.855 139.069 0.590 0.5563
## PopulationVA 27.489 6.428 145.350 4.276 3.42e-05 ***
## PopulationYE 4.227 6.424 145.516 0.658 0.5116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.586
## PopulatinKA -0.698 0.409
## PopulatinMA -0.696 0.408 0.486
## PopulatinMU -0.708 0.415 0.494 0.493
## PopulatinRE -0.648 0.379 0.452 0.451 0.459
## PopulatinUM -0.657 0.385 0.459 0.457 0.465 0.425
## PopulatinVA -0.701 0.410 0.489 0.487 0.496 0.454 0.460
## PopulatinYE -0.701 0.411 0.489 0.488 0.496 0.454 0.461 0.491
anova(DT_A_M_lmer_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 7928.7 991.09 8 141.78 3.337 0.001575 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Schmidt)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population)
## Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Schmidt", ]
##
## REML criterion at convergence: 12831.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8636 -0.4961 -0.1229 0.3065 7.0181
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 416.9 20.42
## Residual 297.0 17.23
## Number of obs: 1464, groups: Line:Population, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 226.01305 4.74292 135.96600 47.653 < 2e-16 ***
## PopulationGI 17.10410 8.12581 145.23581 2.105 0.037021 *
## PopulationKA -6.09966 6.80201 136.33867 -0.897 0.371436
## PopulationMA 2.72810 6.84174 138.66550 0.399 0.690695
## PopulationMU 0.70330 6.79893 136.35220 0.103 0.917763
## PopulationRE 5.63782 7.36602 143.79829 0.765 0.445298
## PopulationUM 3.09168 7.23539 135.25026 0.427 0.669840
## PopulationVA 24.03542 6.84235 139.51268 3.513 0.000598 ***
## PopulationYE -0.07554 6.79153 142.33291 -0.011 0.991141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.584
## PopulatinKA -0.697 0.407
## PopulatinMA -0.693 0.405 0.483
## PopulatinMU -0.698 0.407 0.486 0.484
## PopulatinRE -0.644 0.376 0.449 0.446 0.449
## PopulatinUM -0.656 0.383 0.457 0.454 0.457 0.422
## PopulatinVA -0.693 0.405 0.483 0.481 0.484 0.446 0.454
## PopulatinYE -0.698 0.408 0.487 0.484 0.487 0.450 0.458 0.484
anova(DT_A_F_lmer_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 14440 1805 8 153.46 5.9074 1.365e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_StamenkovicRadak)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "StamenkovicRadak", ]
##
## REML criterion at convergence: 54203.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5991 -0.5815 -0.1328 0.3761 6.5733
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 148.74 12.196
## Line:Population (Intercept) 175.07 13.231
## Batch (Intercept) 40.54 6.367
## Residual 305.55 17.480
## Number of obs: 6200, groups:
## ReplicateVial:Line, 494; Line:Population, 165; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 237.62349 4.73424 10.93403 50.193 2.8e-14 ***
## PopulationGI 7.46572 5.50910 152.34033 1.355 0.177372
## PopulationKA -8.47287 4.88982 151.57662 -1.733 0.085173 .
## PopulationMA 8.62696 4.90527 153.46660 1.759 0.080619 .
## PopulationMU -0.06432 4.88517 151.27795 -0.013 0.989512
## PopulationRE 17.72312 5.18652 152.11258 3.417 0.000812 ***
## PopulationUM 15.29379 5.03496 153.01586 3.038 0.002805 **
## PopulationVA 14.15780 4.96021 152.63502 2.854 0.004913 **
## PopulationYE 2.45664 4.91461 154.56893 0.500 0.617882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.469
## PopulatinKA -0.530 0.454
## PopulatinMA -0.526 0.452 0.510
## PopulatinMU -0.530 0.454 0.513 0.511
## PopulatinRE -0.500 0.427 0.484 0.482 0.484
## PopulatinUM -0.515 0.440 0.499 0.495 0.498 0.471
## PopulatinVA -0.521 0.450 0.504 0.504 0.505 0.475 0.489
## PopulatinYE -0.528 0.452 0.511 0.507 0.510 0.482 0.496 0.502
anova(DT_A_M_lmer_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 15603 1950.3 8 153.79 5.9197 1.315e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_StamenkovicRadak)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "StamenkovicRadak", ]
##
## REML criterion at convergence: 51282.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9954 -0.5866 -0.1542 0.3744 6.6404
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 152.27 12.340
## Line:Population (Intercept) 162.25 12.738
## Batch (Intercept) 37.14 6.094
## Residual 329.47 18.151
## Number of obs: 5815, groups:
## ReplicateVial:Line, 494; Line:Population, 165; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 242.718 4.584 11.349 52.952 6e-15 ***
## PopulationGI 9.006 5.388 152.718 1.671 0.096703 .
## PopulationKA -7.893 4.781 151.809 -1.651 0.100849
## PopulationMA 7.749 4.789 152.779 1.618 0.107700
## PopulationMU 1.406 4.775 151.260 0.294 0.768857
## PopulationRE 16.917 5.070 152.105 3.337 0.001065 **
## PopulationUM 14.365 4.933 154.211 2.912 0.004124 **
## PopulationVA 16.666 4.847 152.554 3.438 0.000755 ***
## PopulationYE 3.325 4.805 154.681 0.692 0.490005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.473
## PopulatinKA -0.535 0.454
## PopulatinMA -0.532 0.452 0.510
## PopulatinMU -0.535 0.454 0.512 0.512
## PopulatinRE -0.505 0.426 0.484 0.482 0.484
## PopulatinUM -0.519 0.438 0.497 0.495 0.497 0.470
## PopulatinVA -0.526 0.449 0.504 0.504 0.505 0.475 0.488
## PopulatinYE -0.532 0.452 0.510 0.507 0.510 0.481 0.495 0.502
anova(DT_A_F_lmer_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1778.2 222.28 8 161.19 1.9922 0.05051 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_F_lmer_Zwaan)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_F[d_DT_A_F$Supervisor.PI == "Zwaan", ]
##
## REML criterion at convergence: 55980.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4229 -0.5259 -0.1848 0.2790 6.6947
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 8.246 2.872
## Line:Population (Intercept) 25.504 5.050
## Batch (Intercept) 6.708 2.590
## Residual 111.578 10.563
## Number of obs: 7330, groups:
## ReplicateVial:Line, 521; Line:Population, 169; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 220.7481 2.2133 2.0039 99.735 9.9e-05 ***
## PopulationGI 4.1186 1.9094 158.6186 2.157 0.03251 *
## PopulationKA 2.5714 1.7615 157.0943 1.460 0.14634
## PopulationMA 3.4714 1.7605 157.1010 1.972 0.05038 .
## PopulationMU 1.3777 1.7767 161.8964 0.775 0.43921
## PopulationRE 5.5128 1.8672 156.8347 2.952 0.00364 **
## PopulationUM 1.6009 1.8048 155.7336 0.887 0.37641
## PopulationVA 4.2001 1.7671 159.0337 2.377 0.01865 *
## PopulationYE 0.5322 1.8015 167.9852 0.295 0.76803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.365
## PopulatinKA -0.396 0.459
## PopulatinMA -0.396 0.460 0.498
## PopulatinMU -0.393 0.456 0.494 0.494
## PopulatinRE -0.374 0.436 0.470 0.471 0.466
## PopulatinUM -0.387 0.449 0.486 0.487 0.482 0.459
## PopulatinVA -0.395 0.458 0.496 0.497 0.492 0.469 0.485
## PopulatinYE -0.387 0.450 0.487 0.487 0.483 0.460 0.476 0.485
anova(DT_A_M_lmer_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 2206.9 275.87 8 160.63 2.2503 0.02641 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DT_A_M_lmer_Zwaan)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DT_EggAdult ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: d_DT_A_M[d_DT_A_M$Supervisor.PI == "Zwaan", ]
##
## REML criterion at convergence: 49447.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9922 -0.5259 -0.1942 0.2460 6.2037
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 7.491 2.737
## Line:Population (Intercept) 29.635 5.444
## Batch (Intercept) 8.828 2.971
## Residual 122.593 11.072
## Number of obs: 6394, groups:
## ReplicateVial:Line, 519; Line:Population, 169; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 225.7358 2.4877 1.8667 90.740 0.000204 ***
## PopulationGI 3.5977 2.0493 159.6267 1.756 0.081085 .
## PopulationKA 1.2331 1.8806 154.7829 0.656 0.512985
## PopulationMA 2.0196 1.8879 156.9895 1.070 0.286371
## PopulationMU -0.1906 1.8954 159.3747 -0.101 0.920023
## PopulationRE 4.1796 2.0077 158.3623 2.082 0.038968 *
## PopulationUM -0.6705 1.9316 154.5842 -0.347 0.728972
## PopulationVA 2.0535 1.8936 158.6298 1.084 0.279793
## PopulationYE -2.6216 1.9294 168.1729 -1.359 0.176034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.348
## PopulatinKA -0.379 0.460
## PopulatinMA -0.378 0.459 0.500
## PopulatinMU -0.376 0.457 0.498 0.496
## PopulatinRE -0.355 0.434 0.470 0.469 0.467
## PopulatinUM -0.369 0.449 0.488 0.487 0.485 0.458
## PopulatinVA -0.377 0.457 0.498 0.496 0.494 0.467 0.485
## PopulatinYE -0.370 0.450 0.489 0.487 0.485 0.459 0.477 0.486
There are 3 populations and 22 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Colinet Lab : Sapho-Lou Marti , Hervé Colinet
Hoedjes Lab : Hristina Kostic, Katja Hoedjes
Onder Lab : Seda Coskun, Senel Selin Senkal, Dogus Can, Banu Sebnem Onder
Read data in R
d_DW <- read.csv("MasterSheets_Oct21_git/DW_MasterSheet_Oct21.csv")
str(d_DW)
## 'data.frame': 24161 obs. of 12 variables:
## $ Supervisor.PI: chr "Colinet" "Colinet" "Colinet" "Colinet" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DW_micrograms: num 0.394 0.473 0.581 0.594 0.454 0.486 0.581 0.584 0.596 0.573 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_DW$Supervisor.PI <- as.factor(d_DW$Supervisor.PI)
d_DW$Diet <- as.factor(d_DW$Diet)
d_DW$Batch <- as.factor(d_DW$Batch)
d_DW$Population <- as.factor(d_DW$Population)
d_DW$Population_Lat <- factor(d_DW$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_DW$Population_Lon <- factor(d_DW$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_DW$Population_Alt <- factor(d_DW$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_DW$Line <- as.factor(d_DW$Line)
d_DW$Sex <- as.factor(d_DW$Sex)
d_DW$DW_micrograms <- as.numeric(d_DW$DW_micrograms)
str(d_DW)
## 'data.frame': 24161 obs. of 15 variables:
## $ Supervisor.PI : Factor w/ 3 levels "Colinet","Hoedjes",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : Factor w/ 170 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DW_micrograms : num 0.394 0.473 0.581 0.594 0.454 0.486 0.581 0.584 0.596 0.573 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat: Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon: Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt: Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_DW_F <-subset(d_DW,Sex=='F')
d_DW_M <-subset(d_DW,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_DW <- min(d_DW$DW_micrograms)
max_DW <- max(d_DW$DW_micrograms)
y-axis is scaled by the minimum (0.136) and maximum (0.73) values in the full data set.
anova(DW_F_lmer_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.0485 0.0060625 8 153.93 2.2495 0.0267 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_F_lmer_Colinet)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_DW_F[d_DW_F$Supervisor.PI == "Colinet", ]
##
## REML criterion at convergence: -11771.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2178 -0.6128 0.0273 0.6249 4.1344
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 0.001328 0.03645
## Batch (Intercept) 0.000219 0.01480
## Residual 0.002695 0.05191
## Number of obs: 3982, groups: Population:Line, 166; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.46881 0.01205 5.84387 38.893 2.79e-08 ***
## PopulationGI 0.01068 0.01336 157.01990 0.799 0.425384
## PopulationKA 0.02967 0.01248 156.90419 2.378 0.018592 *
## PopulationMA 0.03116 0.01216 155.02754 2.562 0.011353 *
## PopulationMU 0.01577 0.01200 154.99934 1.314 0.190908
## PopulationRE 0.04597 0.01336 156.97998 3.441 0.000742 ***
## PopulationUM 0.03221 0.01295 156.97058 2.488 0.013890 *
## PopulationVA 0.02804 0.01248 156.90419 2.247 0.026005 *
## PopulationYE 0.01217 0.01200 155.02544 1.014 0.312021
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.458
## PopulatinKA -0.490 0.497
## PopulatinMA -0.492 0.448 0.480
## PopulatinMU -0.498 0.449 0.481 0.493
## PopulatinRE -0.458 0.462 0.497 0.448 0.449
## PopulatinUM -0.472 0.477 0.515 0.462 0.464 0.478
## PopulatinVA -0.490 0.497 0.537 0.480 0.481 0.497 0.515
## PopulatinYE -0.498 0.449 0.481 0.493 0.500 0.449 0.464 0.481
anova(DW_M_lmer_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.027535 0.0034419 8 157.01 4.8067 2.632e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# could drop batch, as it explains nothing
summary(DW_M_lmer_Colinet)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_DW_M[d_DW_M$Supervisor.PI == "Colinet", ]
##
## REML criterion at convergence: -17014.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6407 -0.5828 -0.0246 0.5790 7.4085
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 0.0003446 0.01856
## Batch (Intercept) 0.0000000 0.00000
## Residual 0.0007161 0.02676
## Number of obs: 3975, groups: Population:Line, 166; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.595e-01 4.327e-03 1.570e+02 59.966 <2e-16 ***
## PopulationGI 9.349e-04 6.612e-03 1.572e+02 0.141 0.8877
## PopulationKA 1.965e-02 6.119e-03 1.570e+02 3.211 0.0016 **
## PopulationMA 1.168e-02 6.199e-03 1.570e+02 1.884 0.0614 .
## PopulationMU 1.264e-02 6.119e-03 1.570e+02 2.066 0.0405 *
## PopulationRE 1.658e-02 6.609e-03 1.570e+02 2.508 0.0131 *
## PopulationUM 1.636e-02 6.383e-03 1.570e+02 2.563 0.0113 *
## PopulationVA 8.075e-03 6.119e-03 1.570e+02 1.320 0.1889
## PopulationYE -1.028e-02 6.119e-03 1.570e+02 -1.680 0.0949 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.654
## PopulatinKA -0.707 0.463
## PopulatinMA -0.698 0.457 0.494
## PopulatinMU -0.707 0.463 0.500 0.494
## PopulatinRE -0.655 0.428 0.463 0.457 0.463
## PopulatinUM -0.678 0.444 0.479 0.473 0.479 0.444
## PopulatinVA -0.707 0.463 0.500 0.494 0.500 0.463 0.479
## PopulatinYE -0.707 0.463 0.500 0.494 0.500 0.463 0.479 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
anova(DW_F_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.0346 0.0043249 8 157.12 1.9203 0.06045 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_F_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_DW_F[d_DW_F$Supervisor.PI == "Hoedjes", ]
##
## REML criterion at convergence: -12532.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6857 -0.6210 -0.0043 0.6319 3.4401
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 1.272e-03 0.035659
## Batch (Intercept) 8.069e-05 0.008983
## Residual 2.252e-03 0.047457
## Number of obs: 4004, groups: Population:Line, 167; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.374e-01 9.404e-03 3.422e+01 46.507 < 2e-16 ***
## PopulationGI 1.093e-02 1.264e-02 1.572e+02 0.865 0.38857
## PopulationKA 2.145e-02 1.169e-02 1.567e+02 1.835 0.06837 .
## PopulationMA 5.763e-03 1.169e-02 1.567e+02 0.493 0.62262
## PopulationMU 1.938e-02 1.168e-02 1.566e+02 1.658 0.09923 .
## PopulationRE 2.810e-02 1.264e-02 1.572e+02 2.223 0.02761 *
## PopulationUM 1.259e-02 1.220e-02 1.569e+02 1.032 0.30359
## PopulationVA 3.573e-02 1.169e-02 1.568e+02 3.057 0.00263 **
## PopulationYE 4.792e-03 1.170e-02 1.574e+02 0.409 0.68276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.574
## PopulatinKA -0.621 0.462
## PopulatinMA -0.621 0.462 0.500
## PopulatinMU -0.621 0.462 0.500 0.500
## PopulatinRE -0.574 0.428 0.462 0.462 0.462
## PopulatinUM -0.595 0.443 0.479 0.479 0.479 0.445
## PopulatinVA -0.621 0.462 0.500 0.500 0.500 0.462 0.479
## PopulatinYE -0.620 0.461 0.500 0.499 0.500 0.460 0.477 0.499
anova(DW_M_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.0033803 0.00042254 8 155.21 0.6263 0.7548
summary(DW_M_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_DW_M[d_DW_M$Supervisor.PI == "Hoedjes", ]
##
## REML criterion at convergence: -17309.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1189 -0.6261 -0.0298 0.6056 5.1645
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 3.883e-04 0.019704
## Batch (Intercept) 9.299e-05 0.009643
## Residual 6.746e-04 0.025974
## Number of obs: 3997, groups: Population:Line, 167; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.637e-01 6.342e-03 1.453e+01 41.576 <2e-16 ***
## PopulationGI 2.232e-03 6.987e-03 1.552e+02 0.319 0.7499
## PopulationKA 1.077e-02 6.455e-03 1.549e+02 1.669 0.0972 .
## PopulationMA 3.160e-03 6.456e-03 1.550e+02 0.490 0.6252
## PopulationMU 5.546e-03 6.457e-03 1.550e+02 0.859 0.3917
## PopulationRE 6.302e-03 6.993e-03 1.560e+02 0.901 0.3688
## PopulationUM 1.735e-03 6.739e-03 1.552e+02 0.257 0.7972
## PopulationVA 8.651e-03 6.460e-03 1.553e+02 1.339 0.1825
## PopulationYE 1.040e-03 6.460e-03 1.551e+02 0.161 0.8723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.474
## PopulatinKA -0.509 0.463
## PopulatinMA -0.511 0.463 0.500
## PopulatinMU -0.509 0.462 0.500 0.500
## PopulatinRE -0.470 0.429 0.463 0.463 0.462
## PopulatinUM -0.487 0.444 0.479 0.480 0.479 0.444
## PopulatinVA -0.513 0.463 0.500 0.500 0.500 0.463 0.479
## PopulatinYE -0.511 0.463 0.500 0.500 0.500 0.462 0.478 0.500
anova(DW_F_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.11338 0.014173 8 150.87 5.3596 6.168e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_F_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_DW_F[d_DW_F$Supervisor.PI == "Onder", ]
##
## REML criterion at convergence: -12234.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7744 -0.5882 0.0568 0.6380 3.6377
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 0.0011087 0.03330
## Batch (Intercept) 0.0002207 0.01485
## Residual 0.0026444 0.05142
## Number of obs: 4102, groups: Population:Line, 166; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.254e-01 1.032e-02 1.779e+01 41.206 < 2e-16 ***
## PopulationGI 1.338e-02 1.205e-02 1.521e+02 1.110 0.268763
## PopulationKA 3.495e-02 1.123e-02 1.540e+02 3.113 0.002211 **
## PopulationMA 4.429e-02 1.104e-02 1.494e+02 4.012 9.50e-05 ***
## PopulationMU 1.512e-02 1.104e-02 1.491e+02 1.369 0.172928
## PopulationRE 4.440e-02 1.183e-02 1.519e+02 3.754 0.000247 ***
## PopulationUM 3.807e-02 1.170e-02 1.542e+02 3.254 0.001398 **
## PopulationVA 4.794e-02 1.106e-02 1.499e+02 4.333 2.68e-05 ***
## PopulationYE 3.795e-03 1.136e-02 1.497e+02 0.334 0.738687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.500
## PopulatinKA -0.543 0.474
## PopulatinMA -0.535 0.458 0.492
## PopulatinMU -0.539 0.462 0.498 0.500
## PopulatinRE -0.508 0.446 0.483 0.467 0.471
## PopulatinUM -0.523 0.451 0.494 0.472 0.479 0.461
## PopulatinVA -0.542 0.465 0.502 0.499 0.502 0.472 0.482
## PopulatinYE -0.523 0.448 0.483 0.486 0.488 0.458 0.465 0.487
anova(DW_M_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.014208 0.001776 8 147.4 1.9511 0.05659 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(DW_M_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DW_micrograms ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_DW_M[d_DW_M$Supervisor.PI == "Onder", ]
##
## REML criterion at convergence: -16576.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2689 -0.6120 -0.0072 0.6481 4.0118
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 4.359e-04 0.020877
## Batch (Intercept) 9.033e-05 0.009504
## Residual 9.103e-04 0.030171
## Number of obs: 4101, groups: Population:Line, 166; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.255121 0.006505 16.364942 39.220 <2e-16 ***
## PopulationGI -0.003951 0.007512 148.441698 -0.526 0.5997
## PopulationKA 0.007906 0.006996 150.347382 1.130 0.2602
## PopulationMA 0.016584 0.006883 146.065835 2.409 0.0172 *
## PopulationMU 0.006807 0.006883 145.768741 0.989 0.3243
## PopulationRE 0.009746 0.007372 148.371553 1.322 0.1882
## PopulationUM 0.012761 0.007291 150.560217 1.750 0.0821 .
## PopulationVA 0.011227 0.006898 146.525460 1.628 0.1057
## PopulationYE -0.002872 0.007079 146.340028 -0.406 0.6855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.494
## PopulatinKA -0.537 0.474
## PopulatinMA -0.529 0.458 0.492
## PopulatinMU -0.533 0.462 0.498 0.500
## PopulatinRE -0.503 0.446 0.482 0.467 0.471
## PopulatinUM -0.517 0.450 0.493 0.472 0.479 0.460
## PopulatinVA -0.536 0.465 0.502 0.499 0.501 0.472 0.482
## PopulatinYE -0.518 0.448 0.483 0.486 0.488 0.458 0.465 0.487
There are 161 populations and 9 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Kozeretska Lab : Svitlana Serga, Alexandra Protsenko, Oleksandr Maistrenko, Iryna Kozeretska
Posnien Lab : Micael Reis, Lennart Hüper
Ritchie Lab : Megan Mcgunnigle, Nicola Cook, Teresa Abaurrea, Michael Ritchie
Schmidt Lab : Amy Goldfischer, Paul Schmidt
Read data in R
d_TL <- read.csv("MasterSheets_Oct21_git/TL_MasterSheet_Feb22.csv")
str(d_TL)
## 'data.frame': 13754 obs. of 14 variables:
## $ Supervisor.PI : chr "Ritchie" "Ritchie" "Ritchie" "Ritchie" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 15 15 1 1 1 2 1 1 1 1 ...
## $ Population : chr "GI" "GI" "KA" "KA" ...
## $ Line : chr "GI5" "GI5" "KA1" "KA1" ...
## $ Sex : chr "M" "M" "M" "M" ...
## $ ReplicateVialOld: int 3 1 3 2 2 2 2 2 2 2 ...
## $ ReplicateVial : chr "Ritchie_15_GI5_3" "Ritchie_15_GI5_1" "Ritchie_1_KA1_3" "Ritchie_1_KA1_2" ...
## $ Individual : int 1117 1060 1210 1170 1169 51 1182 1173 1172 1171 ...
## $ TL_micrometers : num 300 340 460 514 515 521 552 558 565 580 ...
## $ Country : chr "Spain" "Spain" "Denmark" "Denmark" ...
## $ Latitude : num 41.6 41.6 55.9 55.9 55.9 ...
## $ Longitude : num 0.62 0.62 10.21 10.21 10.21 ...
## $ Altitude : int 173 173 15 15 15 173 15 15 15 15 ...
str(d_TL)
## 'data.frame': 13754 obs. of 14 variables:
## $ Supervisor.PI : chr "Ritchie" "Ritchie" "Ritchie" "Ritchie" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 15 15 1 1 1 2 1 1 1 1 ...
## $ Population : chr "GI" "GI" "KA" "KA" ...
## $ Line : chr "GI5" "GI5" "KA1" "KA1" ...
## $ Sex : chr "M" "M" "M" "M" ...
## $ ReplicateVialOld: int 3 1 3 2 2 2 2 2 2 2 ...
## $ ReplicateVial : chr "Ritchie_15_GI5_3" "Ritchie_15_GI5_1" "Ritchie_1_KA1_3" "Ritchie_1_KA1_2" ...
## $ Individual : int 1117 1060 1210 1170 1169 51 1182 1173 1172 1171 ...
## $ TL_micrometers : num 300 340 460 514 515 521 552 558 565 580 ...
## $ Country : chr "Spain" "Spain" "Denmark" "Denmark" ...
## $ Latitude : num 41.6 41.6 55.9 55.9 55.9 ...
## $ Longitude : num 0.62 0.62 10.21 10.21 10.21 ...
## $ Altitude : int 173 173 15 15 15 173 15 15 15 15 ...
d_TL$Supervisor.PI <- as.factor(d_TL$Supervisor.PI)
d_TL$Diet <- as.factor(d_TL$Diet)
d_TL$Batch <- as.factor(d_TL$Batch)
d_TL$Population_Lat <- factor(d_TL$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_TL$Population_Lon <- factor(d_TL$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_TL$Population_Alt <- factor(d_TL$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_TL$Line <- as.factor(d_TL$Line)
d_TL$Sex <- as.factor(d_TL$Sex)
d_TL$ReplicateVial <- as.factor(d_TL$ReplicateVial)
d_TL$TL_micrometers <- as.numeric(d_TL$TL_micrometers)
str(d_TL)
## 'data.frame': 13754 obs. of 17 variables:
## $ Supervisor.PI : Factor w/ 4 levels "Kozeretska","Posnien",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Diet : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
## $ Batch : Factor w/ 12 levels "1","2","3","4",..: 12 12 1 1 1 2 1 1 1 1 ...
## $ Population : chr "GI" "GI" "KA" "KA" ...
## $ Line : Factor w/ 166 levels "AK1","AK10","AK11",..: 31 31 36 36 36 21 36 36 36 36 ...
## $ Sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ ReplicateVialOld: int 3 1 3 2 2 2 2 2 2 2 ...
## $ ReplicateVial : Factor w/ 503 levels "Kozeretska_1_AK1_1",..: 298 296 271 270 270 306 270 270 270 270 ...
## $ Individual : int 1117 1060 1210 1170 1169 51 1182 1173 1172 1171 ...
## $ TL_micrometers : num 300 340 460 514 515 521 552 558 565 580 ...
## $ Country : chr "Spain" "Spain" "Denmark" "Denmark" ...
## $ Latitude : num 41.6 41.6 55.9 55.9 55.9 ...
## $ Longitude : num 0.62 0.62 10.21 10.21 10.21 ...
## $ Altitude : int 173 173 15 15 15 173 15 15 15 15 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 3 3 7 7 7 3 7 7 7 7 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 2 2 3 3 3 2 3 3 3 3 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 3 3 1 1 1 3 1 1 1 1 ...
d_TL_F <-subset(d_TL,Sex=='F')
d_TL_M <-subset(d_TL,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_TL <- min(d_TL$TL_micrometers)
max_TL <- max(d_TL$TL_micrometers)
y-axis is scaled by the minimum (300) and maximum (1232) values in the full data set.
anova(TL_F_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 46443 5805.4 8 119.33 2.7643 0.007735 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_F_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line:Population)
## Data: (subset(d_TL_F, Supervisor.PI == "Kozeretska"))
##
## REML criterion at convergence: 51045.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4816 -0.6029 0.0635 0.6779 3.5476
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 948.80 30.803
## Line:Population (Intercept) 768.84 27.728
## Batch (Intercept) 16.12 4.015
## Residual 2100.14 45.827
## Number of obs: 4810, groups:
## ReplicateVial:Line:Population, 241; Line:Population, 130; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 936.544 8.986 32.423 104.222 <2e-16 ***
## PopulationGI -1.508 15.049 123.353 -0.100 0.9204
## PopulationKA -24.921 13.441 116.126 -1.854 0.0663 .
## PopulationMA 27.752 12.426 123.675 2.233 0.0273 *
## PopulationMU 6.347 12.310 120.143 0.516 0.6071
## PopulationRE 25.851 13.643 121.381 1.895 0.0605 .
## PopulationUM 4.918 13.538 118.603 0.363 0.7171
## PopulationVA 21.821 11.915 118.745 1.831 0.0695 .
## PopulationYE 13.870 13.395 123.600 1.036 0.3024
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.537
## PopulatinKA -0.602 0.359
## PopulatinMA -0.650 0.388 0.435
## PopulatinMU -0.657 0.392 0.439 0.475
## PopulatinRE -0.593 0.354 0.396 0.428 0.433
## PopulatinUM -0.597 0.356 0.399 0.432 0.436 0.393
## PopulatinVA -0.679 0.405 0.454 0.490 0.495 0.447 0.450
## PopulatinYE -0.603 0.360 0.403 0.436 0.440 0.397 0.400 0.455
anova(TL_M_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 43426 5428.3 8 120.17 2.9842 0.004377 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_M_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch) +
## (1 | ReplicateVial:Line:Population)
## Data: (subset(d_TL_M, Supervisor.PI == "Kozeretska"))
##
## REML criterion at convergence: 50338.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8281 -0.5822 0.0434 0.6572 7.9545
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 902.23 30.037
## Line:Population (Intercept) 672.48 25.932
## Batch (Intercept) 11.11 3.333
## Residual 1819.03 42.650
## Number of obs: 4807, groups:
## ReplicateVial:Line:Population, 241; Line:Population, 130; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 835.238 8.442 38.635 98.940 <2e-16 ***
## PopulationGI -4.009 14.310 124.262 -0.280 0.7798
## PopulationKA -22.734 12.772 116.890 -1.780 0.0777 .
## PopulationMA 27.562 11.816 124.613 2.333 0.0213 *
## PopulationMU 2.935 11.702 120.984 0.251 0.8024
## PopulationRE 24.606 12.970 122.241 1.897 0.0602 .
## PopulationUM 3.492 12.868 119.413 0.271 0.7866
## PopulationVA 23.225 11.325 119.573 2.051 0.0425 *
## PopulationYE 10.507 12.737 124.515 0.825 0.4110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.543
## PopulatinKA -0.609 0.359
## PopulatinMA -0.658 0.388 0.435
## PopulatinMU -0.665 0.392 0.439 0.475
## PopulatinRE -0.600 0.354 0.397 0.428 0.433
## PopulatinUM -0.605 0.357 0.400 0.432 0.436 0.394
## PopulatinVA -0.687 0.405 0.454 0.491 0.496 0.447 0.451
## PopulatinYE -0.611 0.360 0.404 0.436 0.440 0.397 0.401 0.455
anova(TL_F_lmer_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 24414 3051.8 8 18 1.9851 0.1082
summary(TL_F_lmer_Posnien)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## TL_micrometers ~ Population + (1 | Line:Population) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_TL_F, Supervisor.PI == "Posnien"))
##
## REML criterion at convergence: 2722.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86894 -0.60033 0.05782 0.63035 2.78455
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 463.9 21.54
## ReplicateVial:Line:Population (Intercept) 542.6 23.29
## Residual 1537.3 39.21
## Number of obs: 270, groups:
## Line:Population, 27; ReplicateVial:Line:Population, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 880.664 19.666 18.000 44.782 <2e-16 ***
## PopulationGI 63.713 27.811 18.000 2.291 0.0343 *
## PopulationKA 47.941 27.811 18.000 1.724 0.1019
## PopulationMA 1.333 27.811 18.000 0.048 0.9623
## PopulationMU -14.433 27.811 18.000 -0.519 0.6101
## PopulationRE 22.108 27.811 18.000 0.795 0.4370
## PopulationUM 27.766 27.811 18.000 0.998 0.3313
## PopulationVA 17.765 27.811 18.000 0.639 0.5310
## PopulationYE 61.181 27.811 18.000 2.200 0.0411 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707
## PopulatinKA -0.707 0.500
## PopulatinMA -0.707 0.500 0.500
## PopulatinMU -0.707 0.500 0.500 0.500
## PopulatinRE -0.707 0.500 0.500 0.500 0.500
## PopulatinUM -0.707 0.500 0.500 0.500 0.500 0.500
## PopulatinVA -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## PopulatinYE -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
anova(TL_M_lmer_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 22105 2763.1 8 18 2.274 0.07047 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_M_lmer_Posnien)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population)
## Data: (subset(d_TL_M, Supervisor.PI == "Posnien"))
##
## REML criterion at convergence: 2661.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7213 -0.5390 -0.0356 0.5975 3.7202
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 814.9 28.55
## Residual 1215.1 34.86
## Number of obs: 270, groups: Line:Population, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 777.155 17.667 18.000 43.988 <2e-16 ***
## PopulationGI 64.073 24.986 18.000 2.564 0.0195 *
## PopulationKA 46.404 24.986 18.000 1.857 0.0797 .
## PopulationMA 8.276 24.986 18.000 0.331 0.7443
## PopulationMU -20.491 24.986 18.000 -0.820 0.4229
## PopulationRE 25.437 24.986 18.000 1.018 0.3221
## PopulationUM 44.733 24.986 18.000 1.790 0.0902 .
## PopulationVA 19.513 24.986 18.000 0.781 0.4450
## PopulationYE 42.405 24.986 18.000 1.697 0.1069
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707
## PopulatinKA -0.707 0.500
## PopulatinMA -0.707 0.500 0.500
## PopulatinMU -0.707 0.500 0.500 0.500
## PopulatinRE -0.707 0.500 0.500 0.500 0.500
## PopulatinUM -0.707 0.500 0.500 0.500 0.500 0.500
## PopulatinVA -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## PopulatinYE -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
anova(TL_F_lmer_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 10416 2083.2 5 12.944 0.7397 0.6073
summary(TL_F_lmer_Ritchie)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: (subset(d_TL_F, Supervisor.PI == "Ritchie"))
##
## REML criterion at convergence: 11444.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7332 -0.5260 0.0394 0.6188 5.1914
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 776.8 27.87
## Batch (Intercept) 913.4 30.22
## Residual 2816.3 53.07
## Number of obs: 1059, groups: Line:Population, 26; Batch, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 955.312 19.715 19.553 48.456 <2e-16 ***
## PopulationGI 14.119 19.934 10.225 0.708 0.495
## PopulationKA 1.041 26.849 19.182 0.039 0.969
## PopulationMA 21.660 26.819 19.097 0.808 0.429
## PopulationMU 15.464 31.445 17.499 0.492 0.629
## PopulationYE 33.399 22.153 13.622 1.508 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU
## PopulatinGI -0.584
## PopulatinKA -0.727 0.430
## PopulatinMA -0.728 0.430 0.765
## PopulatinMU -0.584 0.370 0.537 0.538
## PopulatinYE -0.651 0.538 0.491 0.492 0.484
anova(TL_M_lmer_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 11596 2319.3 5 12.762 0.648 0.6682
summary(TL_M_lmer_Ritchie)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: (subset(d_TL_M, Supervisor.PI == "Ritchie"))
##
## REML criterion at convergence: 11309.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.8016 -0.5065 0.0525 0.5479 3.8390
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 824.6 28.72
## Batch (Intercept) 1396.0 37.36
## Residual 3579.3 59.83
## Number of obs: 1024, groups: Line:Population, 26; Batch, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 826.886 22.014 19.340 37.561 <2e-16 ***
## PopulationGI -3.848 20.744 10.402 -0.185 0.856
## PopulationKA -11.823 30.050 19.192 -0.393 0.698
## PopulationMA 13.633 30.055 19.206 0.454 0.655
## PopulationMU 17.666 34.006 17.335 0.519 0.610
## PopulationYE 19.335 23.473 13.320 0.824 0.425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU
## PopulatinGI -0.547
## PopulatinKA -0.719 0.402
## PopulatinMA -0.718 0.402 0.797
## PopulatinMU -0.582 0.359 0.560 0.560
## PopulatinYE -0.625 0.534 0.476 0.476 0.491
anova(TL_F_lmer_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 38709 4838.7 8 146.7 3.837 0.000399 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(TL_F_lmer_Schmidt)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TL_micrometers ~ Population + (1 | Line:Population)
## Data: (subset(d_TL_F, Supervisor.PI == "Schmidt"))
##
## REML criterion at convergence: 15278.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.8431 -0.5418 0.0956 0.6004 2.7028
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 436.8 20.90
## Residual 1261.1 35.51
## Number of obs: 1514, groups: Line:Population, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 995.047 5.305 143.475 187.553 < 2e-16 ***
## PopulationGI 26.903 8.516 147.382 3.159 0.00192 **
## PopulationKA 10.337 7.599 143.310 1.360 0.17587
## PopulationMA 19.845 7.632 145.365 2.600 0.01028 *
## PopulationMU 9.724 7.512 144.073 1.295 0.19756
## PopulationRE 41.671 8.349 148.572 4.991 1.66e-06 ***
## PopulationUM 16.035 8.124 144.736 1.974 0.05032 .
## PopulationVA 17.287 7.834 143.937 2.207 0.02891 *
## PopulationYE 14.839 7.547 146.651 1.966 0.05116 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.623
## PopulatinKA -0.698 0.435
## PopulatinMA -0.695 0.433 0.485
## PopulatinMU -0.706 0.440 0.493 0.491
## PopulatinRE -0.635 0.396 0.444 0.442 0.449
## PopulatinUM -0.653 0.407 0.456 0.454 0.461 0.415
## PopulatinVA -0.677 0.422 0.473 0.471 0.478 0.430 0.442
## PopulatinYE -0.703 0.438 0.491 0.489 0.497 0.447 0.459 0.476
There are 4 populations and 6 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
pdf(file="ThoraxLength/p_TL_F_lab_cor.pdf",width=8, height=8)
p_TL_F_lab_cor()
invisible(dev.off())
pdf(file="ThoraxLength/p_TL_M_lab_cor.pdf",width=8, height=8)
p_TL_M_lab_cor()
invisible(dev.off())
For a detailed description of tables, plots, linear models and outputs, please refer here
Onder Lab : Cansu Aksoy, Ekin Demir, Ezgi Cobanoglu, Banu Sebnem Onder
Posnien Lab : Micael Reis, Lennart Hüper, Nico Posnien
Ritchie Lab : Megan Mcgunnigle, Nicola Cook, Teresa Abaurrea, Marija Tanaskovic, Michael Ritchie
Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Filip Filopovski, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak
d_WA <- read.csv("MasterSheets_Oct21_git/WA_MasterSheet_Nov21.csv")
str(d_WA)
## 'data.frame': 21201 obs. of 15 variables:
## $ Supervisor.PI : chr "StamenkovicRadak" "StamenkovicRadak" "StamenkovicRadak" "StamenkovicRadak" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "YE" "YE" "YE" "YE" ...
## $ Line : chr "YE13" "YE13" "YE13" "YE13" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateVialOld : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : chr "StamenkovicRadak_1_YE13_1" "StamenkovicRadak_1_YE13_1" "StamenkovicRadak_1_YE13_1" "StamenkovicRadak_1_YE13_1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ CentroidSizeLeft_micrometers : num 2869 2910 2901 3050 2906 ...
## $ CentroidSizeRight_micrometers: num 2891 2889 2881 3039 2919 ...
## $ Country : chr "Turkey" "Turkey" "Turkey" "Turkey" ...
## $ Latitude : num 40.2 40.2 40.2 40.2 40.2 ...
## $ Longitude : num 32.3 32.3 32.3 32.3 32.3 ...
## $ Altitude : int 680 680 680 680 680 680 680 680 680 680 ...
d_WA$Supervisor.PI <- as.factor(d_WA$Supervisor.PI)
d_WA$Diet <- as.factor(d_WA$Diet)
d_WA$Batch <- as.factor(d_WA$Batch)
d_WA$Population_Lat <- factor(d_WA$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_WA$Population_Lon <- factor(d_WA$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_WA$Population_Alt <- factor(d_WA$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_WA$Line <- as.factor(d_WA$Line)
d_WA$Sex <- as.factor(d_WA$Sex)
d_WA$ReplicateVial <- as.factor(d_WA$ReplicateVial)
d_WA$CentroidSizeLeft_micrometers <- as.numeric(d_WA$CentroidSizeLeft_micrometers)
d_WA$CentroidSizeRight_micrometers <- as.numeric(d_WA$CentroidSizeRight_micrometers)
str(d_WA)
## 'data.frame': 21201 obs. of 18 variables:
## $ Supervisor.PI : Factor w/ 4 levels "Onder","Posnien",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ Diet : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 15 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "YE" "YE" "YE" "YE" ...
## $ Line : Factor w/ 172 levels "AK1","AK10","AK11",..: 154 154 154 154 154 154 154 154 154 154 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVialOld : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : Factor w/ 806 levels "Onder_1_AK1_1",..: 711 711 711 711 711 711 711 711 711 711 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ CentroidSizeLeft_micrometers : num 2869 2910 2901 3050 2906 ...
## $ CentroidSizeRight_micrometers: num 2891 2889 2881 3039 2919 ...
## $ Country : chr "Turkey" "Turkey" "Turkey" "Turkey" ...
## $ Latitude : num 40.2 40.2 40.2 40.2 40.2 ...
## $ Longitude : num 32.3 32.3 32.3 32.3 32.3 ...
## $ Altitude : int 680 680 680 680 680 680 680 680 680 680 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 8 8 8 8 8 8 8 8 8 8 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
d_WA_F <-subset(d_WA,Sex=='F')
d_WA_M <-subset(d_WA,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_WAL <- min(d_WA$CentroidSizeLeft_micrometers)
max_WAL <- max(d_WA$CentroidSizeLeft_micrometers)
y-axis is scaled by the minimum (1857.612491) and maximum (3333.0502) values in the full data set.
anova(WA_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 416463 52058 8 151.87 8.9484 5.178e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_L_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: d_WA_F %>% filter(Supervisor.PI == "Onder")
##
## REML criterion at convergence: 61105.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7289 -0.5621 0.0553 0.6205 4.0410
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 781.8 27.96
## Line:Population (Intercept) 3090.7 55.59
## Batch (Intercept) 1209.3 34.77
## Residual 5817.5 76.27
## Number of obs: 5247, groups:
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2813.825 19.103 14.957 147.300 < 2e-16 ***
## PopulationGI 39.068 20.578 152.357 1.899 0.0595 .
## PopulationKA 44.852 19.374 155.067 2.315 0.0219 *
## PopulationMA 84.984 18.845 150.624 4.510 1.30e-05 ***
## PopulationMU 46.278 18.935 151.042 2.444 0.0157 *
## PopulationRE 130.045 20.225 152.488 6.430 1.55e-09 ***
## PopulationUM 87.275 19.956 153.548 4.373 2.25e-05 ***
## PopulationVA 29.681 18.989 151.898 1.563 0.1201
## PopulationYE 1.476 19.176 151.285 0.077 0.9387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.459
## PopulatinKA -0.505 0.469
## PopulatinMA -0.488 0.457 0.485
## PopulatinMU -0.506 0.460 0.503 0.497
## PopulatinRE -0.473 0.441 0.484 0.463 0.476
## PopulatinUM -0.487 0.449 0.497 0.468 0.483 0.464
## PopulatinVA -0.499 0.465 0.511 0.497 0.505 0.477 0.486
## PopulatinYE -0.495 0.459 0.501 0.490 0.498 0.472 0.480 0.499
anova(WA_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 294608 36826 8 155.79 7.8374 7.989e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_L_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "Onder"))
##
## REML criterion at convergence: 59806.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2993 -0.5881 0.0332 0.6103 5.8833
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 765.6 27.67
## Line:Population (Intercept) 2085.2 45.66
## Batch (Intercept) 336.5 18.34
## Residual 4698.8 68.55
## Number of obs: 5230, groups:
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2444.130 13.489 29.295 181.192 < 2e-16 ***
## PopulationGI 32.102 17.250 156.168 1.861 0.06464 .
## PopulationKA 34.147 16.224 158.411 2.105 0.03690 *
## PopulationMA 65.104 15.816 154.657 4.116 6.24e-05 ***
## PopulationMU 35.036 15.873 154.491 2.207 0.02877 *
## PopulationRE 101.188 16.978 157.147 5.960 1.60e-08 ***
## PopulationUM 69.106 16.725 157.292 4.132 5.83e-05 ***
## PopulationVA 44.498 15.923 155.777 2.795 0.00585 **
## PopulationYE -3.993 16.090 155.387 -0.248 0.80432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.546
## PopulatinKA -0.599 0.468
## PopulatinMA -0.581 0.458 0.486
## PopulatinMU -0.600 0.460 0.503 0.497
## PopulatinRE -0.560 0.440 0.482 0.463 0.475
## PopulatinUM -0.577 0.448 0.495 0.469 0.483 0.463
## PopulatinVA -0.593 0.465 0.510 0.497 0.505 0.476 0.485
## PopulatinYE -0.588 0.459 0.500 0.490 0.498 0.470 0.480 0.499
anova(WA_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 96035 12004 8 18 1.4376 0.2476
summary(WA_F_L_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_F, Supervisor.PI == "Posnien"))
##
## REML criterion at convergence: 3169.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3762 -0.5835 -0.0034 0.6637 2.5365
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 7243.4 85.11
## ReplicateVial:Line:Population (Intercept) 432.2 20.79
## Residual 8350.2 91.38
## Number of obs: 270, groups:
## Line:Population, 27; ReplicateVial:Line:Population, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2589.01 53.26 18.00 48.609 <2e-16 ***
## PopulationGI 139.19 75.32 18.00 1.848 0.0811 .
## PopulationKA 50.37 75.32 18.00 0.669 0.5122
## PopulationMA 69.49 75.32 18.00 0.923 0.3684
## PopulationMU -25.56 75.32 18.00 -0.339 0.7383
## PopulationRE 134.92 75.32 18.00 1.791 0.0901 .
## PopulationUM 74.02 75.32 18.00 0.983 0.3388
## PopulationVA -39.43 75.32 18.00 -0.523 0.6070
## PopulationYE 68.71 75.32 18.00 0.912 0.3737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707
## PopulatinKA -0.707 0.500
## PopulatinMA -0.707 0.500 0.500
## PopulatinMU -0.707 0.500 0.500 0.500
## PopulatinRE -0.707 0.500 0.500 0.500 0.500
## PopulatinUM -0.707 0.500 0.500 0.500 0.500 0.500
## PopulatinVA -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## PopulatinYE -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
anova(WA_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 36700 4587.5 8 18 0.7652 0.637
summary(WA_M_L_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "Posnien"))
##
## REML criterion at convergence: 3086.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9072 -0.4977 0.0192 0.6488 3.4055
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 6707.76 81.90
## ReplicateVial:Line:Population (Intercept) 72.92 8.54
## Residual 5995.06 77.43
## Number of obs: 270, groups:
## Line:Population, 27; ReplicateVial:Line:Population, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2300.02 49.60 18.00 46.372 <2e-16 ***
## PopulationGI 111.76 70.14 18.00 1.593 0.128
## PopulationKA 32.56 70.14 18.00 0.464 0.648
## PopulationMA 81.32 70.14 18.00 1.159 0.261
## PopulationMU -33.44 70.14 18.00 -0.477 0.639
## PopulationRE 74.92 70.14 18.00 1.068 0.300
## PopulationUM 48.33 70.14 18.00 0.689 0.500
## PopulationVA 39.70 70.14 18.00 0.566 0.578
## PopulationYE 49.17 70.14 18.00 0.701 0.492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707
## PopulatinKA -0.707 0.500
## PopulatinMA -0.707 0.500 0.500
## PopulatinMU -0.707 0.500 0.500 0.500
## PopulatinRE -0.707 0.500 0.500 0.500 0.500
## PopulatinUM -0.707 0.500 0.500 0.500 0.500 0.500
## PopulatinVA -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## PopulatinYE -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
anova(WA_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 161188 20148 8 27.027 2.2953 0.05084 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_L_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_F, Supervisor.PI == "Ritchie"))
##
## REML criterion at convergence: 16563.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3958 -0.5231 0.0337 0.6233 3.1535
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 6184 78.64
## Line:Population (Intercept) 17974 134.07
## Batch (Intercept) 1663 40.79
## Residual 8778 93.69
## Number of obs: 1367, groups:
## ReplicateVial:Line:Population, 132; Line:Population, 47; Batch, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2759.24 74.08 27.97 37.245 <2e-16 ***
## PopulationGI -172.06 90.56 29.41 -1.900 0.0673 .
## PopulationKA -45.09 104.69 27.85 -0.431 0.6700
## PopulationMA 87.43 99.36 27.78 0.880 0.3864
## PopulationMU 23.66 99.29 27.88 0.238 0.8134
## PopulationRE 119.37 99.23 27.93 1.203 0.2391
## PopulationUM 129.78 99.22 27.99 1.308 0.2015
## PopulationVA 15.01 98.49 28.09 0.152 0.8799
## PopulationYE 23.58 97.55 27.05 0.242 0.8108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.783
## PopulatinKA -0.708 0.554
## PopulatinMA -0.746 0.584 0.558
## PopulatinMU -0.746 0.584 0.551 0.583
## PopulatinRE -0.746 0.584 0.544 0.570 0.564
## PopulatinUM -0.745 0.585 0.528 0.557 0.563 0.576
## PopulatinVA -0.741 0.585 0.525 0.553 0.553 0.573 0.578
## PopulatinYE -0.735 0.602 0.521 0.549 0.555 0.550 0.561 0.552
anova(WA_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 53563 6695.4 8 27.745 0.9646 0.4828
summary(WA_M_L_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "Ritchie"))
##
## REML criterion at convergence: 16229.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9391 -0.4829 0.0696 0.5871 4.6442
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 4539.8 67.38
## Line:Population (Intercept) 6326.1 79.54
## Batch (Intercept) 740.3 27.21
## Residual 6941.1 83.31
## Number of obs: 1368, groups:
## ReplicateVial:Line:Population, 132; Line:Population, 44; Batch, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2448.31 46.86 35.17 52.245 <2e-16 ***
## PopulationGI -65.05 60.56 24.42 -1.074 0.293
## PopulationKA -22.46 62.88 35.11 -0.357 0.723
## PopulationMA 29.70 62.87 35.07 0.472 0.640
## PopulationMU -11.90 62.81 35.11 -0.189 0.851
## PopulationRE 47.67 62.73 35.02 0.760 0.452
## PopulationUM 63.32 62.76 35.13 1.009 0.320
## PopulationVA -16.05 62.25 34.25 -0.258 0.798
## PopulationYE -35.16 61.62 30.97 -0.571 0.572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.723
## PopulatinKA -0.745 0.539
## PopulatinMA -0.745 0.539 0.592
## PopulatinMU -0.746 0.540 0.585 0.585
## PopulatinRE -0.746 0.540 0.571 0.571 0.565
## PopulatinUM -0.745 0.540 0.556 0.557 0.563 0.578
## PopulatinVA -0.740 0.543 0.552 0.552 0.553 0.575 0.581
## PopulatinYE -0.734 0.554 0.548 0.548 0.555 0.549 0.562 0.552
## boundary (singular) fit: see ?isSingular
## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e-03
anova(WA_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 624737 78092 8 114.6 10.088 1.429e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_L_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_F, Supervisor.PI == "StamenkovicRadak"))
##
## REML criterion at convergence: 44876.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4785 -0.5666 0.0599 0.6399 3.6361
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 2434.129 49.34
## Line:Population (Intercept) 6.451 2.54
## Batch (Intercept) 0.000 0.00
## Residual 7741.151 87.98
## Number of obs: 3787, groups:
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2780.82 12.95 113.51 214.712 < 2e-16 ***
## PopulationGI 55.22 20.92 114.35 2.639 0.009472 **
## PopulationKA 29.48 18.36 114.56 1.606 0.111023
## PopulationMA 81.80 20.33 114.43 4.023 0.000103 ***
## PopulationMU 66.96 18.60 113.14 3.599 0.000475 ***
## PopulationRE 151.99 19.37 114.23 7.845 2.53e-12 ***
## PopulationUM 102.14 19.01 114.75 5.373 4.12e-07 ***
## PopulationVA 57.63 18.64 114.01 3.092 0.002503 **
## PopulationYE 41.29 18.98 113.99 2.176 0.031645 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.619
## PopulatinKA -0.705 0.437
## PopulatinMA -0.637 0.394 0.449
## PopulatinMU -0.696 0.431 0.491 0.443
## PopulatinRE -0.668 0.414 0.472 0.426 0.465
## PopulatinUM -0.681 0.422 0.481 0.434 0.474 0.455
## PopulatinVA -0.695 0.430 0.490 0.443 0.484 0.464 0.473
## PopulatinYE -0.682 0.422 0.481 0.435 0.475 0.456 0.465 0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#could drop batch, as it explains nothing
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
## Warning: Model failed to converge with 1 negative eigenvalue: -4.0e-03
anova(WA_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 457239 57155 8 112.21 9.5541 5.168e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_L_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeLeft_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "StamenkovicRadak"))
##
## REML criterion at convergence: 42455.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.2393 -0.5570 0.0480 0.6236 6.0054
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 0.3221 0.5676
## Line:Population (Intercept) 1857.6981 43.1010
## Batch (Intercept) 6.6839 2.5853
## Residual 5982.2379 77.3449
## Number of obs: 3662, groups:
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2415.63 11.41 58.45 211.661 < 2e-16 ***
## PopulationGI 56.51 18.30 112.86 3.088 0.00253 **
## PopulationKA 19.10 16.06 112.79 1.189 0.23692
## PopulationMA 74.48 17.80 113.38 4.185 5.66e-05 ***
## PopulationMU 52.79 16.28 110.79 3.243 0.00156 **
## PopulationRE 121.95 16.98 113.27 7.184 7.65e-11 ***
## PopulationUM 91.59 16.63 113.64 5.506 2.31e-07 ***
## PopulationVA 45.84 16.30 109.93 2.812 0.00584 **
## PopulationYE 31.86 16.62 112.43 1.917 0.05774 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.615
## PopulatinKA -0.702 0.437
## PopulatinMA -0.635 0.396 0.450
## PopulatinMU -0.692 0.431 0.491 0.444
## PopulatinRE -0.664 0.413 0.472 0.426 0.465
## PopulatinUM -0.678 0.422 0.482 0.435 0.475 0.456
## PopulatinVA -0.690 0.430 0.490 0.442 0.483 0.464 0.473
## PopulatinYE -0.678 0.422 0.482 0.435 0.475 0.456 0.465 0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_WAR <- min(d_WA$CentroidSizeRight_micrometers)
max_WAR <- max(d_WA$CentroidSizeRight_micrometers)
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
anova(WA_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 421367 52671 8 151.53 9.0579 3.977e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_R_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_F, Supervisor.PI == "Onder"))
##
## REML criterion at convergence: 61106
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6211 -0.5609 0.0470 0.6255 3.7505
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 806.6 28.40
## Line:Population (Intercept) 3014.8 54.91
## Batch (Intercept) 1097.0 33.12
## Residual 5814.9 76.26
## Number of obs: 5247, groups:
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2815.3200 18.5805 15.4593 151.520 < 2e-16 ***
## PopulationGI 39.4891 20.3804 152.0176 1.938 0.0545 .
## PopulationKA 44.7269 19.1888 154.5953 2.331 0.0211 *
## PopulationMA 84.3472 18.6644 150.3413 4.519 1.25e-05 ***
## PopulationMU 45.0927 18.7527 150.6648 2.405 0.0174 *
## PopulationRE 129.3083 20.0313 152.1392 6.455 1.37e-09 ***
## PopulationUM 86.5324 19.7651 153.1872 4.378 2.21e-05 ***
## PopulationVA 29.8798 18.8068 151.5711 1.589 0.1142
## PopulationYE 0.3856 18.9925 150.9798 0.020 0.9838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.468
## PopulatinKA -0.514 0.469
## PopulatinMA -0.497 0.457 0.485
## PopulatinMU -0.515 0.460 0.503 0.497
## PopulatinRE -0.481 0.441 0.484 0.463 0.476
## PopulatinUM -0.496 0.449 0.497 0.468 0.483 0.464
## PopulatinVA -0.508 0.465 0.511 0.497 0.505 0.477 0.486
## PopulatinYE -0.504 0.459 0.501 0.490 0.498 0.472 0.480 0.499
anova(WA_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 288086 36011 8 156.05 7.6824 1.188e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_R_Onder_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "Onder"))
##
## REML criterion at convergence: 59827.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1368 -0.5848 0.0327 0.6114 6.0665
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 874.6 29.57
## Line:Population (Intercept) 2102.4 45.85
## Batch (Intercept) 282.9 16.82
## Residual 4687.4 68.46
## Number of obs: 5230, groups:
## ReplicateVial:Line:Population, 518; Line:Population, 167; Batch, 7
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2444.990 13.290 35.827 183.969 < 2e-16 ***
## PopulationGI 32.847 17.427 156.488 1.885 0.06131 .
## PopulationKA 36.345 16.381 158.647 2.219 0.02793 *
## PopulationMA 64.809 15.983 154.890 4.055 7.92e-05 ***
## PopulationMU 34.311 16.034 154.618 2.140 0.03393 *
## PopulationRE 102.099 17.153 157.513 5.952 1.66e-08 ***
## PopulationUM 68.797 16.894 157.624 4.072 7.35e-05 ***
## PopulationVA 44.416 16.087 156.079 2.761 0.00645 **
## PopulationYE -3.748 16.257 155.670 -0.231 0.81799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.559
## PopulatinKA -0.614 0.468
## PopulatinMA -0.596 0.458 0.487
## PopulatinMU -0.615 0.461 0.503 0.497
## PopulatinRE -0.574 0.440 0.482 0.463 0.475
## PopulatinUM -0.591 0.448 0.495 0.470 0.483 0.462
## PopulatinVA -0.608 0.465 0.510 0.497 0.505 0.476 0.485
## PopulatinYE -0.602 0.459 0.500 0.490 0.498 0.470 0.479 0.499
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -1.1e-04
anova(WA_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 97450 12181 8 18 1.4012 0.2615
summary(WA_F_R_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_F, Supervisor.PI == "Posnien"))
##
## REML criterion at convergence: 3180
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.88515 -0.60816 0.01116 0.64507 2.45427
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 7037.2 83.89
## ReplicateVial:Line:Population (Intercept) 718.1 26.80
## Residual 8693.5 93.24
## Number of obs: 270, groups:
## Line:Population, 27; ReplicateVial:Line:Population, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2584.98 53.62 18.00 48.211 <2e-16 ***
## PopulationGI 140.26 75.83 18.00 1.850 0.0808 .
## PopulationKA 52.35 75.83 18.00 0.690 0.4988
## PopulationMA 72.29 75.83 18.00 0.953 0.3530
## PopulationMU -22.66 75.83 18.00 -0.299 0.7685
## PopulationRE 135.06 75.83 18.00 1.781 0.0918 .
## PopulationUM 75.52 75.83 18.00 0.996 0.3325
## PopulationVA -37.05 75.83 18.00 -0.489 0.6310
## PopulationYE 72.22 75.83 18.00 0.952 0.3535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707
## PopulatinKA -0.707 0.500
## PopulatinMA -0.707 0.500 0.500
## PopulatinMU -0.707 0.500 0.500 0.500
## PopulatinRE -0.707 0.500 0.500 0.500 0.500
## PopulatinUM -0.707 0.500 0.500 0.500 0.500 0.500
## PopulatinVA -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## PopulatinYE -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
anova(WA_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 32589 4073.6 8 18 0.6753 0.7071
summary(WA_M_R_Posnien_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "Posnien"))
##
## REML criterion at convergence: 3088.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0851 -0.5566 -0.0153 0.6569 3.5274
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 38.41 6.198
## ReplicateVial:Line:Population (Intercept) 6927.30 83.230
## Residual 6032.05 77.666
## Number of obs: 270, groups:
## Line:Population, 27; ReplicateVial:Line:Population, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2296.73 50.23 18.00 45.725 <2e-16 ***
## PopulationGI 108.33 71.03 18.00 1.525 0.145
## PopulationKA 34.91 71.03 18.00 0.492 0.629
## PopulationMA 86.17 71.03 18.00 1.213 0.241
## PopulationMU -22.87 71.03 18.00 -0.322 0.751
## PopulationRE 78.26 71.03 18.00 1.102 0.285
## PopulationUM 52.40 71.03 18.00 0.738 0.470
## PopulationVA 37.49 71.03 18.00 0.528 0.604
## PopulationYE 48.05 71.03 18.00 0.676 0.507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.707
## PopulatinKA -0.707 0.500
## PopulatinMA -0.707 0.500 0.500
## PopulatinMU -0.707 0.500 0.500 0.500
## PopulatinRE -0.707 0.500 0.500 0.500 0.500
## PopulatinUM -0.707 0.500 0.500 0.500 0.500 0.500
## PopulatinVA -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## PopulatinYE -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
anova(WA_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 163375 20422 8 26.835 2.3305 0.04807 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_R_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_F, Supervisor.PI == "Ritchie"))
##
## REML criterion at convergence: 16564.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4488 -0.5249 0.0615 0.6440 3.1927
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 6492 80.58
## Line:Population (Intercept) 17468 132.17
## Batch (Intercept) 1632 40.39
## Residual 8763 93.61
## Number of obs: 1367, groups:
## ReplicateVial:Line:Population, 132; Line:Population, 47; Batch, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2777.36 73.35 27.73 37.864 <2e-16 ***
## PopulationGI -165.69 89.72 29.22 -1.847 0.0749 .
## PopulationKA -45.51 103.65 27.61 -0.439 0.6640
## PopulationMA 98.04 98.38 27.54 0.997 0.3277
## PopulationMU 28.33 98.31 27.64 0.288 0.7753
## PopulationRE 118.86 98.25 27.69 1.210 0.2366
## PopulationUM 136.27 98.24 27.75 1.387 0.1764
## PopulationVA 16.64 97.51 27.86 0.171 0.8658
## PopulationYE 20.02 96.58 26.86 0.207 0.8374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.782
## PopulatinKA -0.708 0.554
## PopulatinMA -0.746 0.583 0.559
## PopulatinMU -0.746 0.584 0.551 0.583
## PopulatinRE -0.746 0.584 0.544 0.570 0.564
## PopulatinUM -0.745 0.584 0.528 0.557 0.563 0.576
## PopulatinVA -0.741 0.585 0.525 0.553 0.553 0.573 0.579
## PopulatinYE -0.735 0.602 0.521 0.549 0.555 0.550 0.561 0.552
anova(WA_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 57030 7128.7 8 27.792 1.0373 0.4328
summary(WA_M_R_Ritchie_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "Ritchie"))
##
## REML criterion at convergence: 16215.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9851 -0.5112 0.0484 0.5839 4.7200
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 4472.4 66.88
## Line:Population (Intercept) 6265.6 79.16
## Batch (Intercept) 642.8 25.35
## Residual 6872.5 82.90
## Number of obs: 1368, groups:
## ReplicateVial:Line:Population, 132; Line:Population, 44; Batch, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2462.726 46.397 35.179 53.080 <2e-16 ***
## PopulationGI -61.574 60.208 24.479 -1.023 0.316
## PopulationKA -17.058 62.255 35.130 -0.274 0.786
## PopulationMA 36.423 62.238 35.091 0.585 0.562
## PopulationMU -5.495 62.192 35.119 -0.088 0.930
## PopulationRE 53.814 62.121 35.030 0.866 0.392
## PopulationUM 70.115 62.159 35.138 1.128 0.267
## PopulationVA -13.902 61.707 34.271 -0.225 0.823
## PopulationYE -31.154 61.155 31.080 -0.509 0.614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.726
## PopulatinKA -0.745 0.541
## PopulatinMA -0.745 0.541 0.588
## PopulatinMU -0.746 0.542 0.582 0.582
## PopulatinRE -0.746 0.542 0.569 0.570 0.564
## PopulatinUM -0.745 0.542 0.556 0.556 0.562 0.576
## PopulatinVA -0.741 0.544 0.553 0.553 0.553 0.573 0.578
## PopulatinYE -0.736 0.554 0.549 0.549 0.555 0.550 0.561 0.552
## boundary (singular) fit: see ?isSingular
anova(WA_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 652123 81515 8 114.54 10.577 4.982e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_F_R_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_F, Supervisor.PI == "StamenkovicRadak"))
##
## REML criterion at convergence: 44862
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7152 -0.5616 0.0783 0.6287 3.6999
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 2473.834 49.738
## Line:Population (Intercept) 8.832 2.972
## Batch (Intercept) 0.000 0.000
## Residual 7707.008 87.790
## Number of obs: 3787, groups:
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2775.53 13.05 113.47 212.685 < 2e-16 ***
## PopulationGI 58.19 21.08 114.29 2.761 0.006725 **
## PopulationKA 37.52 18.50 114.50 2.029 0.044821 *
## PopulationMA 86.50 20.49 114.37 4.223 4.87e-05 ***
## PopulationMU 74.74 18.75 113.11 3.987 0.000119 ***
## PopulationRE 157.73 19.52 114.18 8.080 7.48e-13 ***
## PopulationUM 111.31 19.16 114.69 5.811 5.67e-08 ***
## PopulationVA 60.93 18.78 113.96 3.244 0.001546 **
## PopulationYE 47.88 19.12 113.95 2.504 0.013710 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.619
## PopulatinKA -0.705 0.437
## PopulatinMA -0.637 0.394 0.449
## PopulatinMU -0.696 0.431 0.491 0.443
## PopulatinRE -0.668 0.414 0.472 0.426 0.465
## PopulatinUM -0.681 0.422 0.481 0.434 0.474 0.455
## PopulatinVA -0.695 0.430 0.490 0.443 0.484 0.465 0.473
## PopulatinYE -0.682 0.422 0.481 0.435 0.475 0.456 0.465 0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
# could drop batch, as it explains nothing
## boundary (singular) fit: see ?isSingular
## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02
anova(WA_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 447007 55876 8 114.12 9.518 5.12e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(WA_M_R_StamenkovicRadak_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CentroidSizeRight_micrometers ~ Population + (1 | Line:Population) +
## (1 | Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_WA_M, Supervisor.PI == "StamenkovicRadak"))
##
## REML criterion at convergence: 42394.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.2672 -0.5646 0.0593 0.6360 5.9328
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 1.971e+03 44.393013
## Line:Population (Intercept) 5.134e+00 2.265812
## Batch (Intercept) 2.508e-06 0.001584
## Residual 5.871e+03 76.619391
## Number of obs: 3662, groups:
## ReplicateVial:Line:Population, 124; Line:Population, 124; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2414.97 11.64 113.02 207.545 < 2e-16 ***
## PopulationGI 54.10 18.78 113.55 2.880 0.00476 **
## PopulationKA 19.86 16.49 113.90 1.205 0.23084
## PopulationMA 73.65 18.26 113.66 4.034 9.97e-05 ***
## PopulationMU 54.91 16.72 112.80 3.284 0.00136 **
## PopulationRE 125.69 17.43 114.26 7.213 6.42e-11 ***
## PopulationUM 94.90 17.07 114.05 5.559 1.81e-07 ***
## PopulationVA 47.87 16.75 113.55 2.858 0.00507 **
## PopulationYE 33.86 17.06 113.79 1.985 0.04960 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.619
## PopulatinKA -0.706 0.437
## PopulatinMA -0.637 0.395 0.450
## PopulatinMU -0.696 0.431 0.491 0.444
## PopulatinRE -0.668 0.414 0.471 0.426 0.465
## PopulatinUM -0.682 0.422 0.481 0.434 0.474 0.455
## PopulatinVA -0.695 0.430 0.490 0.443 0.484 0.464 0.474
## PopulatinYE -0.682 0.422 0.481 0.435 0.475 0.455 0.465 0.474
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
There are 9 populations and 5 isofemale lines for females and 9 populations and 5 isofemale lines for males have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
#Note that the trait has been phenotyped only in females.
Billeter Lab : Xiaocui Wang, Tiphaine Bailly, Mario Mira, Jean-Christophe Billeter
Fricke Lab : Claudia Fricke
Reading data in R
d_Fec <- read.csv("MasterSheets_Oct21_git/FEC_MasterSheet_Oct21.csv")
str(d_Fec)
## 'data.frame': 1725 obs. of 13 variables:
## $ Supervisor.PI : chr "Billeter" "Billeter" "Billeter" "Billeter" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: int 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
Factors need reformatting (i.e. Supervisor.PI should be coded as a factor, not character).
str(d_Fec)
## 'data.frame': 1725 obs. of 13 variables:
## $ Supervisor.PI : chr "Billeter" "Billeter" "Billeter" "Billeter" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: int 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_Fec$Supervisor.PI <- as.factor(d_Fec$Supervisor.PI)
d_Fec$Diet <- as.factor(d_Fec$Diet)
d_Fec$Batch <- as.factor(d_Fec$Batch)
d_Fec$Population <- as.factor(d_Fec$Population)
d_Fec$Population_Lat <- factor(d_Fec$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Fec$Population_Lon <- factor(d_Fec$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Fec$Population_Alt <- factor(d_Fec$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Fec$Line <- as.factor(d_Fec$Line)
d_Fec$NumberOfAdultsEclosed <- as.numeric(d_Fec$NumberOfAdultsEclosed)
d_Fec$Censor <- as.factor(d_Fec$Censor)
str(d_Fec)
## 'data.frame': 1725 obs. of 16 variables:
## $ Supervisor.PI : Factor w/ 2 levels "Billeter","Fricke": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : Factor w/ 169 levels "AK1","AK10","AK11",..: 1 1 1 1 1 11 11 11 11 11 ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: num 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_Fec <- subset(d_Fec, Censor == "0")
Now they should be in the correct format, see below.
str(d_Fec)
## 'data.frame': 1721 obs. of 16 variables:
## $ Supervisor.PI : Factor w/ 2 levels "Billeter","Fricke": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : Factor w/ 169 levels "AK1","AK10","AK11",..: 1 1 1 1 1 11 11 11 11 11 ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: num 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
Descriptive statistics at the line level, with batch information :
Descriptive statistics for fecundity at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_Fec <- min(d_Fec$NumberOfAdultsEclosed)
max_Fec <- max(d_Fec$NumberOfAdultsEclosed)
y-axis is scaled by the minimum (0) and maximum (306) values in the full data set.
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(Fec_lmer_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 61874 7734.2 8 148.39 2.7992 0.006461 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Fec_lmer_Billeter)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NumberOfAdultsEclosed ~ Population + (1 | Population:Line)
## Data: d_Fec[d_Fec$Supervisor.PI == "Billeter", ]
##
## REML criterion at convergence: 8773.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.02323 -0.55643 -0.03579 0.53645 2.97008
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 1113 33.36
## Residual 2763 52.56
## Number of obs: 805, groups: Population:Line, 160
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 97.067 9.154 149.480 10.603 <2e-16 ***
## PopulationGI -22.874 13.904 146.434 -1.645 0.1021
## PopulationKA 12.903 12.928 148.886 0.998 0.3198
## PopulationMA 2.053 13.460 147.925 0.152 0.8790
## PopulationMU -3.225 13.226 152.369 -0.244 0.8077
## PopulationRE -29.213 14.231 148.404 -2.053 0.0419 *
## PopulationUM 5.760 13.959 148.978 0.413 0.6805
## PopulationVA -10.734 12.894 147.238 -0.832 0.4065
## PopulationYE -33.130 12.963 150.368 -2.556 0.0116 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.658
## PopulatinKA -0.708 0.466
## PopulatinMA -0.680 0.448 0.482
## PopulatinMU -0.692 0.456 0.490 0.471
## PopulatinRE -0.643 0.424 0.455 0.437 0.445
## PopulatinUM -0.656 0.432 0.464 0.446 0.454 0.422
## PopulatinVA -0.710 0.467 0.503 0.483 0.491 0.457 0.466
## PopulatinYE -0.706 0.465 0.500 0.480 0.489 0.454 0.463 0.501
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(Fec_lmer_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 4506.3 563.29 8 146.12 0.4525 0.8873
summary(Fec_lmer_Fricke)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NumberOfAdultsEclosed ~ Population + (1 | Population:Line) +
## (1 | Batch)
## Data: d_Fec[d_Fec$Supervisor.PI == "Fricke", ]
##
## REML criterion at convergence: 9249.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8102 -0.6481 -0.0705 0.5894 3.4523
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 455.06 21.332
## Batch (Intercept) 41.26 6.423
## Residual 1244.86 35.283
## Number of obs: 916, groups: Population:Line, 161; Batch, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 61.074 6.779 95.724 9.009 2.05e-14 ***
## PopulationGI -10.583 9.374 152.556 -1.129 0.261
## PopulationKA -10.298 8.769 143.094 -1.174 0.242
## PopulationMA -5.089 8.673 146.815 -0.587 0.558
## PopulationMU -4.207 8.875 147.002 -0.474 0.636
## PopulationRE -3.612 9.302 151.133 -0.388 0.698
## PopulationUM -14.075 8.963 148.011 -1.570 0.118
## PopulationVA -7.192 8.825 149.162 -0.815 0.416
## PopulationYE -8.232 8.793 151.818 -0.936 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.623
## PopulatinKA -0.665 0.483
## PopulatinMA -0.671 0.486 0.522
## PopulatinMU -0.667 0.475 0.511 0.513
## PopulatinRE -0.628 0.455 0.487 0.495 0.478
## PopulatinUM -0.653 0.472 0.505 0.514 0.495 0.479
## PopulatinVA -0.668 0.477 0.513 0.521 0.510 0.484 0.503
## PopulatinYE -0.668 0.480 0.514 0.522 0.507 0.486 0.506 0.511
There are 152 populations and 152 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Flatt Lab : Esra Durmaz, Envel Kerdaffrec, Thibault Schowing, Virginie Thieu, Marisa Rodrigues, Thomas Flatt
Parsch Lab : Amanda Glaser-Schmitt, Eliza Argyridou, John Parsch
Pasyukova Lab : Natalia Roshina, Alexander Symonenko, Mikhail Trostnikov, Evgenia Tsybul’ko, Ekaterina Veselkina, Olga Rybina, Elena Pasyukova
d_LS_L <- read.csv("MasterSheets_Oct21_git/LSL_MasterSheet_Oct21.csv")
str(d_LS_L)
## 'data.frame': 39844 obs. of 15 variables:
## $ Supervisor.PI : chr "Pasyukova" "Pasyukova" "Pasyukova" "Pasyukova" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateVialOld : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : chr "Pasyukova_1_AK1_1" "Pasyukova_1_AK1_1" "Pasyukova_1_AK1_1" "Pasyukova_1_AK1_1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ LSL_AgeAtDeath_days: int 10 37 41 42 42 45 45 45 45 46 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_LS_L$Supervisor.PI <- as.factor(d_LS_L$Supervisor.PI)
d_LS_L$Diet <- as.factor(d_LS_L$Diet)
d_LS_L$Batch <- as.factor(d_LS_L$Batch)
d_LS_L$Population <- as.factor(d_LS_L$Population)
d_LS_L$Population_Lat <- factor(d_LS_L$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LS_L$Population_Lon <- factor(d_LS_L$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LS_L$Population_Alt <- factor(d_LS_L$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LS_L$Line <- as.factor(d_LS_L$Line)
d_LS_L$ReplicateVial <- as.factor(d_LS_L$ReplicateVial)
d_LS_L$LSL_AgeAtDeath_days <- as.numeric(d_LS_L$LSL_AgeAtDeath_days)
d_LS_L$Censor <- as.factor(d_LS_L$Censor)
str(d_LS_L)
## 'data.frame': 39844 obs. of 18 variables:
## $ Supervisor.PI : Factor w/ 2 levels "Parsch","Pasyukova": 2 2 2 2 2 2 2 2 2 2 ...
## $ Diet : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
## $ Batch : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateVialOld : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : Factor w/ 2032 levels "Parsch_1_AK1_1",..: 681 681 681 681 681 681 681 681 681 681 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Censor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ LSL_AgeAtDeath_days: num 10 37 41 42 42 45 45 45 45 46 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_LS_L_F <-subset(d_LS_L,Sex=='F')
d_LS_L_M <-subset(d_LS_L,Sex=='M')
d_LS_P <- read.csv("MasterSheets_Oct21_git/LSP_MasterSheet_Oct21.csv")
str(d_LS_P)
## 'data.frame': 10612 obs. of 14 variables:
## $ Supervisor.PI : chr "Flatt" "Flatt" "Flatt" "Flatt" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateCageOld : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateCage : chr "Flatt_AK_F_1" "Flatt_AK_F_1" "Flatt_AK_F_1" "Flatt_AK_F_1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Censor : int 1 1 1 1 0 0 0 1 0 0 ...
## $ LSP_AgeAtDeath_days: int 11 11 17 23 26 26 33 33 40 43 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_LS_P$Supervisor.PI <- as.factor(d_LS_P$Supervisor.PI)
d_LS_P$Diet <- as.factor(d_LS_P$Diet)
d_LS_P$Batch <- as.factor(d_LS_P$Batch)
d_LS_P$Population <- as.factor(d_LS_P$Population)
d_LS_P$Population_Lat <- factor(d_LS_P$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LS_P$Population_Lon <- factor(d_LS_P$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LS_P$Population_Alt <- factor(d_LS_P$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LS_P$ReplicateCage <- as.factor(d_LS_P$ReplicateCage)
d_LS_P$LSP_AgeAtDeath_days <- as.numeric(d_LS_P$LSP_AgeAtDeath_days)
d_LS_P$Censor <- as.factor(d_LS_P$Censor)
str(d_LS_P)
## 'data.frame': 10612 obs. of 17 variables:
## $ Supervisor.PI : Factor w/ 1 level "Flatt": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateCageOld : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateCage : Factor w/ 180 levels "Flatt_AK_F_1",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Censor : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ...
## $ LSP_AgeAtDeath_days: num 11 11 17 23 26 26 33 33 40 43 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_LS_P_F <-subset(d_LS_P,Sex=='F')
d_LS_P_M <-subset(d_LS_P,Sex=='M')
d_LS_M <- read.csv("MasterSheets_Oct21_git/LSM_MasterSheet_Oct21.csv")
str(d_LS_M)
## 'data.frame': 50456 obs. of 12 variables:
## $ Supervisor.PI : chr "Pasyukova" "Pasyukova" "Pasyukova" "Pasyukova" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ LSM_AgeAtDeath_days: int 10 37 41 42 42 45 45 45 45 46 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_LS_M$Supervisor.PI <- as.factor(d_LS_M$Supervisor.PI)
d_LS_M$Diet <- as.factor(d_LS_M$Diet)
d_LS_M$Batch <- as.factor(d_LS_M$Batch)
d_LS_M$Population <- as.factor(d_LS_M$Population)
d_LS_M$Population_Lat <- factor(d_LS_M$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LS_M$Population_Lon <- factor(d_LS_M$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LS_M$Population_Alt <- factor(d_LS_M$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LS_M$LSM_AgeAtDeath_days <- as.numeric(d_LS_M$LSM_AgeAtDeath_days)
d_LS_M$Censor <- as.factor(d_LS_M$Censor)
str(d_LS_M)
## 'data.frame': 50456 obs. of 15 variables:
## $ Supervisor.PI : Factor w/ 3 levels "Flatt","Parsch",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Diet : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
## $ Batch : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Censor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ LSM_AgeAtDeath_days: num 10 37 41 42 42 45 45 45 45 46 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_LS_M_F <-subset(d_LS_M,Sex=='F')
d_LS_M_M <-subset(d_LS_M,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_LS <- min(d_LS_M$LSM_AgeAtDeath_days)
max_LS <- max(d_LS_M$LSM_AgeAtDeath_days)
y-axis is scaled by the minimum (1) and maximum (112) values in the full data set.
anova(LS_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 48851 6106.4 8 80.887 38.896 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_P_F_Flatt_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LSP_AgeAtDeath_days ~ Population + (1 | Population:ReplicateCage)
## Data: filter(d_LS_P_F, Censor == "0" & Supervisor.PI == "Flatt")
##
## REML criterion at convergence: 36116.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3943 -0.6327 -0.0087 0.6402 3.5292
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:ReplicateCage (Intercept) 5.11 2.261
## Residual 156.99 12.530
## Number of obs: 4567, groups: Population:ReplicateCage, 90
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.0334 0.9132 83.3744 65.740 < 2e-16 ***
## PopulationGI -17.0858 1.2783 80.0349 -13.366 < 2e-16 ***
## PopulationKA -4.6088 1.2802 80.5100 -3.600 0.000548 ***
## PopulationMA -9.5139 1.2873 82.3008 -7.390 1.09e-10 ***
## PopulationMU -2.6133 1.2894 82.8120 -2.027 0.045911 *
## PopulationRE -14.4260 1.2849 81.6893 -11.228 < 2e-16 ***
## PopulationUM -8.3626 1.2968 84.6172 -6.449 6.66e-09 ***
## PopulationVA -3.7623 1.2899 82.8853 -2.917 0.004548 **
## PopulationYE -10.3710 1.2829 81.2162 -8.084 5.05e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.714
## PopulatinKA -0.713 0.510
## PopulatinMA -0.709 0.507 0.506
## PopulatinMU -0.708 0.506 0.505 0.502
## PopulatinRE -0.711 0.508 0.507 0.504 0.503
## PopulatinUM -0.704 0.503 0.502 0.500 0.499 0.500
## PopulatinVA -0.708 0.506 0.505 0.502 0.501 0.503 0.499
## PopulatinYE -0.712 0.508 0.508 0.505 0.504 0.506 0.501 0.504
anova(LS_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 7270.2 908.78 8 80.282 5.1988 3.022e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_P_M_Flatt_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: LSP_AgeAtDeath_days ~ Population + (1 | Population:ReplicateCage)
## Data: filter(d_LS_P_M, Censor == "0" & Supervisor.PI == "Flatt")
##
## REML criterion at convergence: 35650
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9146 -0.6656 -0.0467 0.6755 3.0925
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:ReplicateCage (Intercept) 3.827 1.956
## Residual 174.806 13.221
## Number of obs: 4450, groups: Population:ReplicateCage, 90
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 51.6194 0.8739 86.0482 59.065 < 2e-16 ***
## PopulationGI -0.7746 1.2196 81.5439 -0.635 0.52715
## PopulationKA 4.4450 1.2221 82.2841 3.637 0.00048 ***
## PopulationMA 1.4199 1.2193 81.5433 1.165 0.24759
## PopulationMU 4.0254 1.2309 84.7065 3.270 0.00155 **
## PopulationRE -1.1984 1.2198 81.6147 -0.982 0.32880
## PopulationUM 0.7366 1.2216 82.0648 0.603 0.54817
## PopulationVA 0.4164 1.2399 87.0018 0.336 0.73782
## PopulationYE 0.7089 1.2213 82.0555 0.580 0.56318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.717
## PopulatinKA -0.715 0.512
## PopulatinMA -0.717 0.514 0.513
## PopulatinMU -0.710 0.509 0.508 0.509
## PopulatinRE -0.716 0.513 0.512 0.514 0.509
## PopulatinUM -0.715 0.513 0.512 0.513 0.508 0.513
## PopulatinVA -0.705 0.505 0.504 0.505 0.500 0.505 0.504
## PopulatinYE -0.716 0.513 0.512 0.513 0.508 0.513 0.512 0.504
anova(LS_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 17537 2192.2 8 126.41 8.199 6.785e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_F_Parsch_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +
## (1 | Line:ReplicateVial)
## Data: filter(d_LS_L_F, Censor == "0" & Supervisor.PI == "Parsch")
##
## REML criterion at convergence: 42206.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6820 -0.5293 0.1220 0.6479 2.5964
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:ReplicateVial (Intercept) 46.61 6.827
## Population:Line (Intercept) 21.52 4.639
## Batch (Intercept) 12.92 3.594
## Residual 267.37 16.351
## Number of obs: 4934, groups:
## Line:ReplicateVial, 679; Population:Line, 135; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.625 3.000 1.861 19.878 0.0035 **
## PopulationGI -4.716 2.260 125.623 -2.087 0.0389 *
## PopulationKA -2.759 2.259 125.474 -1.221 0.2243
## PopulationMA -12.319 2.269 127.434 -5.430 2.75e-07 ***
## PopulationMU -5.612 2.259 125.275 -2.484 0.0143 *
## PopulationRE -13.187 2.256 124.844 -5.844 4.16e-08 ***
## PopulationUM -10.542 2.260 125.477 -4.665 7.77e-06 ***
## PopulationVA -3.205 2.250 123.444 -1.424 0.1568
## PopulationYE -5.034 2.264 126.542 -2.223 0.0280 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.374
## PopulatinKA -0.375 0.496
## PopulatinMA -0.373 0.494 0.497
## PopulatinMU -0.375 0.497 0.498 0.496
## PopulatinRE -0.375 0.497 0.499 0.497 0.499
## PopulatinUM -0.375 0.496 0.498 0.496 0.498 0.499
## PopulatinVA -0.376 0.498 0.500 0.498 0.500 0.501 0.500
## PopulatinYE -0.374 0.495 0.497 0.495 0.497 0.498 0.497 0.499
anova(LS_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 16320 2040 8 126.51 5.7368 3.127e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_M_Parsch_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +
## (1 | Line:ReplicateVial)
## Data: filter(d_LS_L_M, Censor == "0" & Supervisor.PI == "Parsch")
##
## REML criterion at convergence: 42301.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2393 -0.5900 0.0897 0.6900 2.7320
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:ReplicateVial (Intercept) 13.150 3.626
## Population:Line (Intercept) 34.973 5.914
## Batch (Intercept) 2.336 1.528
## Residual 355.597 18.857
## Number of obs: 4821, groups:
## Line:ReplicateVial, 679; Population:Line, 135; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.4729 2.0895 11.2354 28.942 6.58e-12 ***
## PopulationGI -9.1042 2.5261 127.0376 -3.604 0.000448 ***
## PopulationKA -3.6781 2.5280 127.3877 -1.455 0.148142
## PopulationMA -9.5146 2.5298 127.6972 -3.761 0.000257 ***
## PopulationMU -1.8715 2.5213 126.0661 -0.742 0.459297
## PopulationRE -11.2522 2.5232 126.4047 -4.460 1.79e-05 ***
## PopulationUM -3.5999 2.5290 127.6173 -1.423 0.157049
## PopulationVA -0.7315 2.5259 126.9086 -0.290 0.772607
## PopulationYE -8.7973 2.5284 127.4703 -3.479 0.000688 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.605
## PopulatinKA -0.606 0.500
## PopulatinMA -0.606 0.500 0.501
## PopulatinMU -0.607 0.502 0.502 0.502
## PopulatinRE -0.607 0.501 0.502 0.502 0.503
## PopulatinUM -0.605 0.500 0.501 0.501 0.502 0.502
## PopulatinVA -0.606 0.501 0.501 0.501 0.502 0.502 0.501
## PopulatinYE -0.606 0.500 0.501 0.501 0.502 0.502 0.501 0.501
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0111326 (tol = 0.002, component 1)
anova(LS_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 4191.7 523.96 8 160.01 2.4971 0.01396 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_F_Pasyukova_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +
## (1 | Line:ReplicateVial)
## Data: filter(d_LS_L_F, Censor == "0" & Supervisor.PI == "Pasyukova")
##
## REML criterion at convergence: 112305.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4209 -0.5713 0.0810 0.6441 3.8347
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:ReplicateVial (Intercept) 39.803 6.309
## Population:Line (Intercept) 24.786 4.979
## Batch (Intercept) 2.699 1.643
## Residual 209.826 14.485
## Number of obs: 13520, groups:
## Line:ReplicateVial, 1352; Population:Line, 169; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 43.97758 1.68064 3.98971 26.167 1.3e-05 ***
## PopulationGI -0.13317 1.99074 160.00824 -0.067 0.947
## PopulationKA 2.78562 1.75818 160.00824 1.584 0.115
## PopulationMA -0.36000 1.75818 160.00824 -0.205 0.838
## PopulationMU -0.05625 1.75818 160.00824 -0.032 0.975
## PopulationRE 0.20156 1.86975 160.00824 0.108 0.914
## PopulationUM -4.65347 1.80862 160.00824 -2.573 0.011 *
## PopulationVA 0.07812 1.75818 160.00824 0.044 0.965
## PopulationYE -2.67250 1.75818 160.00824 -1.520 0.130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.440
## PopulatinKA -0.498 0.421
## PopulatinMA -0.498 0.421 0.476
## PopulatinMU -0.498 0.421 0.476 0.476
## PopulatinRE -0.468 0.395 0.448 0.448 0.448
## PopulatinUM -0.484 0.409 0.463 0.463 0.463 0.435
## PopulatinVA -0.498 0.421 0.476 0.476 0.476 0.448 0.463
## PopulatinYE -0.498 0.421 0.476 0.476 0.476 0.448 0.463 0.476
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0111326 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00449959 (tol = 0.002, component 1)
anova(LS_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 9879 1234.9 8 159.99 4.4281 7.308e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LS_L_M_Pasyukova_lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## LSL_AgeAtDeath_days ~ Population + (1 | Batch) + (1 | Population:Line) +
## (1 | Line:ReplicateVial)
## Data: filter(d_LS_L_M, Censor == "0" & Supervisor.PI == "Pasyukova")
##
## REML criterion at convergence: 116093.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0485 -0.5537 0.0609 0.6453 2.8822
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:ReplicateVial (Intercept) 49.623 7.044
## Population:Line (Intercept) 31.736 5.633
## Batch (Intercept) 1.325 1.151
## Residual 278.870 16.699
## Number of obs: 13520, groups:
## Line:ReplicateVial, 1352; Population:Line, 169; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.4761 1.5965 11.8203 31.616 8.68e-13 ***
## PopulationGI -2.7135 2.2515 159.9923 -1.205 0.229908
## PopulationKA 3.0332 1.9885 159.9923 1.525 0.129145
## PopulationMA -2.9168 1.9885 159.9923 -1.467 0.144384
## PopulationMU 0.1282 1.9885 159.9923 0.064 0.948683
## PopulationRE -2.1693 2.1147 159.9923 -1.026 0.306523
## PopulationUM -6.8617 2.0456 159.9923 -3.354 0.000993 ***
## PopulationVA -1.1931 1.9885 159.9923 -0.600 0.549368
## PopulationYE -6.0003 1.9885 159.9934 -3.017 0.002966 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.524
## PopulatinKA -0.593 0.421
## PopulatinMA -0.593 0.421 0.476
## PopulatinMU -0.593 0.421 0.476 0.476
## PopulatinRE -0.558 0.395 0.448 0.448 0.448
## PopulatinUM -0.577 0.409 0.463 0.463 0.463 0.435
## PopulatinVA -0.593 0.421 0.476 0.476 0.476 0.448 0.463
## PopulatinYE -0.593 0.421 0.476 0.476 0.476 0.448 0.463 0.476
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00449959 (tol = 0.002, component 1)
#Please refer to "_SurvivalAnalyses_" folder
There are 0 populations and 133 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Gonzalez Lab : Llewellyn Green, Josefa Gonzalez, Miriam Merenciano
Kozeretska Lab : Svitlana Serga, Alexandra Protsenko, Oleksandr Maistrenko, Iryna Kozeretska
Vieira Lab : Jorge Vieira, Cristina P. Vieira, Pedro Duque, Tânia Dias
d_CSM <- read.csv("MasterSheets_Oct21_git/CSM_MasterSheet_Oct21.csv")
str(d_CSM)
## 'data.frame': 2584 obs. of 16 variables:
## $ Supervisor.PI : chr "Gonzalez" "Gonzalez" "Gonzalez" "Gonzalez" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 1 2 3 1 3 4 1 2 3 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK10" ...
## $ Sex : chr "M" "M" "M" "M" ...
## $ ReplicateVialOld: int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : chr "Gonzalez_1_AK1_M_1" "Gonzalez_2_AK1_M_2" "Gonzalez_3_AK1_M_3" "Gonzalez_1_AK10_M_1" ...
## $ Total : int 20 19 18 20 20 20 16 20 18 16 ...
## $ Dead : int 14 11 3 16 4 11 8 17 4 16 ...
## $ CSM_PropDead : num 70 57.9 16.7 80 20 55 50 85 22.2 100 ...
## $ CSM_PropDead_ED : num 0.7 0.579 0.167 0.8 0.2 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_CSM$Supervisor.PI <- as.factor(d_CSM$Supervisor.PI)
d_CSM$Diet <- as.factor(d_CSM$Diet)
d_CSM$Batch <- as.factor(d_CSM$Batch)
d_CSM$Population_Lat <- factor(d_CSM$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_CSM$Population_Lon <- factor(d_CSM$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_CSM$Population_Alt <- factor(d_CSM$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_CSM$Line <- as.factor(d_CSM$Line)
d_CSM$Sex <- as.factor(d_CSM$Sex)
d_CSM$ReplicateVial <- as.factor(d_CSM$ReplicateVial)
d_CSM$CSM_PropDead_ED <- as.numeric(d_CSM$CSM_PropDead_ED)
str(d_CSM)
## 'data.frame': 2584 obs. of 19 variables:
## $ Supervisor.PI : Factor w/ 3 levels "Gonzalez","Kozeretska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
## $ Batch : Factor w/ 32 levels "1","2","3","4",..: 1 2 3 1 3 4 1 2 3 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 172 levels "AK1","AK10","AK11",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ Sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ ReplicateVialOld: int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : Factor w/ 2584 levels "Gonzalez_1_AK1_F_1",..: 2 152 342 4 344 584 6 154 346 8 ...
## $ Total : int 20 19 18 20 20 20 16 20 18 16 ...
## $ Dead : int 14 11 3 16 4 11 8 17 4 16 ...
## $ CSM_PropDead : num 70 57.9 16.7 80 20 55 50 85 22.2 100 ...
## $ CSM_PropDead_ED : num 0.7 0.579 0.167 0.8 0.2 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_CSM_F <-subset(d_CSM,Sex=='F')
d_CSM_M <-subset(d_CSM,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_CSM <- min(d_CSM$CSM_PropDead_ED)
max_CSM <- max(d_CSM$CSM_PropDead_ED)
y-axis is scaled by the minimum (0) and maximum (1) values in the full data set.
anova(CSM_F_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.51553 0.064441 8 147.32 1.9571 0.05577 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CSM_F_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: d_CSM_F_trans[d_CSM_F_trans$Supervisor.PI == "Gonzalez", ]
##
## REML criterion at convergence: -72
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7096 -0.5792 -0.0380 0.6031 2.6103
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.02233 0.1494
## Batch (Intercept) 0.01094 0.1046
## Residual 0.03293 0.1815
## Number of obs: 544, groups: Line:Population, 160; Batch, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.32326 0.05395 34.64144 24.529 < 2e-16 ***
## PopulationGI -0.17210 0.06168 148.74549 -2.790 0.00596 **
## PopulationKA -0.08811 0.06098 141.06586 -1.445 0.15070
## PopulationMA -0.08720 0.05703 147.11430 -1.529 0.12841
## PopulationMU -0.03564 0.05701 147.02810 -0.625 0.53281
## PopulationRE -0.11019 0.06032 144.50429 -1.827 0.06981 .
## PopulationUM -0.19304 0.06271 145.34606 -3.078 0.00249 **
## PopulationVA -0.11783 0.05672 144.00044 -2.078 0.03953 *
## PopulationYE -0.06171 0.05682 143.73777 -1.086 0.27934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.478
## PopulatinKA -0.488 0.425
## PopulatinMA -0.525 0.454 0.461
## PopulatinMU -0.520 0.454 0.460 0.491
## PopulatinRE -0.506 0.429 0.438 0.469 0.465
## PopulatinUM -0.474 0.413 0.418 0.448 0.446 0.424
## PopulatinVA -0.517 0.456 0.460 0.491 0.493 0.464 0.447
## PopulatinYE -0.533 0.456 0.466 0.498 0.494 0.476 0.451 0.492
anova(CSM_M_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.66328 0.082911 8 147.95 1.7495 0.09162 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CSM_M_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: d_CSM_M_trans[d_CSM_M_trans$Supervisor.PI == "Gonzalez", ]
##
## REML criterion at convergence: 107.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2902 -0.5700 -0.0374 0.4998 2.9573
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.02578 0.1606
## Batch (Intercept) 0.03675 0.1917
## Residual 0.04739 0.2177
## Number of obs: 544, groups: Line:Population, 160; Batch, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.890522 0.078853 16.836637 11.293 2.82e-09 ***
## PopulationGI 0.074983 0.068797 149.870804 1.090 0.2775
## PopulationKA 0.098935 0.067867 140.622954 1.458 0.1471
## PopulationMA 0.026821 0.063580 147.725741 0.422 0.6737
## PopulationMU 0.009290 0.063549 147.617182 0.146 0.8840
## PopulationRE 0.004401 0.067228 144.500011 0.065 0.9479
## PopulationUM -0.057059 0.069875 145.388654 -0.817 0.4155
## PopulationVA -0.020614 0.063173 144.041366 -0.326 0.7447
## PopulationYE 0.145985 0.063307 143.509589 2.306 0.0225 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.363
## PopulatinKA -0.372 0.424
## PopulatinMA -0.400 0.453 0.461
## PopulatinMU -0.396 0.452 0.459 0.490
## PopulatinRE -0.387 0.428 0.438 0.468 0.464
## PopulatinUM -0.361 0.411 0.418 0.447 0.445 0.423
## PopulatinVA -0.393 0.454 0.460 0.490 0.492 0.463 0.446
## PopulatinYE -0.407 0.455 0.466 0.498 0.493 0.477 0.450 0.491
anova(CSM_F_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.41646 0.052057 8 113.84 0.4214 0.9061
summary(CSM_F_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: d_CSM_F_trans[d_CSM_F_trans$Supervisor.PI == "Kozeretska", ]
##
## REML criterion at convergence: 251.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98016 -0.61640 -0.09206 0.58901 2.29545
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.02928 0.1711
## Batch (Intercept) 0.03133 0.1770
## Residual 0.12353 0.3515
## Number of obs: 244, groups: Line:Population, 130; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.88456 0.14574 1.60370 6.069 0.0429 *
## PopulationGI -0.07755 0.12814 120.55726 -0.605 0.5462
## PopulationKA -0.13366 0.11571 114.94299 -1.155 0.2505
## PopulationMA -0.12261 0.10605 120.05564 -1.156 0.2499
## PopulationMU -0.07069 0.10447 117.01418 -0.677 0.5000
## PopulationRE -0.10789 0.11427 108.41666 -0.944 0.3472
## PopulationUM -0.18809 0.11681 110.11572 -1.610 0.1102
## PopulationVA -0.06807 0.10038 112.54488 -0.678 0.4990
## PopulationYE -0.06874 0.11236 123.47720 -0.612 0.5418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.288
## PopulatinKA -0.329 0.362
## PopulatinMA -0.337 0.386 0.425
## PopulatinMU -0.341 0.392 0.430 0.477
## PopulatinRE -0.324 0.363 0.408 0.433 0.439
## PopulatinUM -0.306 0.351 0.386 0.427 0.433 0.393
## PopulatinVA -0.349 0.406 0.441 0.498 0.507 0.454 0.452
## PopulatinYE -0.321 0.366 0.404 0.443 0.449 0.410 0.402 0.468
anova(CSM_M_lmer_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.87614 0.10952 8 112.87 0.8066 0.5981
summary(CSM_M_lmer_Kozeretska)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: d_CSM_M_trans[d_CSM_M_trans$Supervisor.PI == "Kozeretska", ]
##
## REML criterion at convergence: 238.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.68234 -0.75897 0.05052 0.69572 1.83245
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.005378 0.07333
## Batch (Intercept) 0.034408 0.18549
## Residual 0.135783 0.36849
## Number of obs: 244, groups: Line:Population, 130; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.141197 0.147492 1.437570 7.737 0.0382 *
## PopulationGI 0.009018 0.115920 121.640216 0.078 0.9381
## PopulationKA -0.142703 0.104274 114.986203 -1.369 0.1738
## PopulationMA -0.049584 0.095842 118.570191 -0.517 0.6059
## PopulationMU -0.127495 0.094278 117.572178 -1.352 0.1789
## PopulationRE -0.088661 0.102357 103.311622 -0.866 0.3884
## PopulationUM -0.130669 0.104837 107.557830 -1.246 0.2153
## PopulationVA -0.084597 0.090287 111.769032 -0.937 0.3508
## PopulationYE 0.050031 0.101826 124.595934 0.491 0.6241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.256
## PopulatinKA -0.295 0.361
## PopulatinMA -0.300 0.385 0.424
## PopulatinMU -0.304 0.391 0.431 0.478
## PopulatinRE -0.291 0.365 0.411 0.436 0.443
## PopulatinUM -0.275 0.353 0.389 0.429 0.436 0.399
## PopulatinVA -0.312 0.407 0.442 0.501 0.510 0.459 0.457
## PopulatinYE -0.285 0.364 0.403 0.441 0.449 0.412 0.403 0.469
anova(CSM_F_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.37712 0.047141 8 28.474 1.5683 0.1786
summary(CSM_F_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: d_CSM_F_trans[d_CSM_F_trans$Supervisor.PI == "Vieira", ]
##
## REML criterion at convergence: -63.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3746 -0.5691 -0.0185 0.6048 2.3916
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.030988 0.17604
## Batch (Intercept) 0.002363 0.04861
## Residual 0.030059 0.17337
## Number of obs: 504, groups: Line:Population, 168; Batch, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.268575 0.049136 40.512400 25.818 <2e-16 ***
## PopulationGI -0.035655 0.076006 92.151141 -0.469 0.6401
## PopulationKA 0.058282 0.070095 34.031699 0.831 0.4115
## PopulationMA -0.002099 0.070558 28.455908 -0.030 0.9765
## PopulationMU 0.004964 0.067787 89.243620 0.073 0.9418
## PopulationRE -0.022527 0.075564 23.529537 -0.298 0.7682
## PopulationUM -0.111670 0.069922 50.602527 -1.597 0.1165
## PopulationVA 0.122112 0.071029 26.096556 1.719 0.0974 .
## PopulationYE 0.016747 0.071084 25.536015 0.236 0.8156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.613
## PopulatinKA -0.701 0.430
## PopulatinMA -0.696 0.427 0.492
## PopulatinMU -0.667 0.431 0.468 0.465
## PopulatinRE -0.650 0.399 0.462 0.453 0.434
## PopulatinUM -0.690 0.433 0.503 0.481 0.483 0.449
## PopulatinVA -0.692 0.424 0.485 0.482 0.461 0.450 0.477
## PopulatinYE -0.691 0.424 0.484 0.487 0.461 0.449 0.477 0.523
anova(CSM_M_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.72875 0.091094 8 52.099 2.4479 0.02494 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CSM_M_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CSM_PropDead_ED ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: d_CSM_M_trans[d_CSM_M_trans$Supervisor.PI == "Vieira", ]
##
## REML criterion at convergence: 86.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.11405 -0.49749 -0.01356 0.56781 2.06627
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.051485 0.22690
## Batch (Intercept) 0.009326 0.09657
## Residual 0.037214 0.19291
## Number of obs: 504, groups: Line:Population, 168; Batch, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.04462 0.06727 63.64605 15.528 < 2e-16 ***
## PopulationGI 0.04585 0.10030 124.28802 0.457 0.64837
## PopulationKA 0.06970 0.09659 58.91769 0.722 0.47342
## PopulationMA 0.01715 0.09829 47.90436 0.175 0.86220
## PopulationMU 0.23817 0.08926 133.67896 2.668 0.00856 **
## PopulationRE 0.05815 0.10633 40.87319 0.547 0.58744
## PopulationUM -0.02656 0.09452 83.92641 -0.281 0.77938
## PopulationVA 0.09561 0.09922 46.76312 0.964 0.34018
## PopulationYE 0.28626 0.09937 46.78454 2.881 0.00597 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.593
## PopulatinKA -0.694 0.413
## PopulatinMA -0.684 0.406 0.486
## PopulatinMU -0.626 0.417 0.438 0.428
## PopulatinRE -0.633 0.375 0.456 0.433 0.396
## PopulatinUM -0.680 0.426 0.514 0.466 0.474 0.432
## PopulatinVA -0.678 0.402 0.471 0.467 0.424 0.429 0.461
## PopulatinYE -0.677 0.401 0.470 0.478 0.424 0.428 0.460 0.557
There are 9 populations and 126 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Mensh Lab : Florencia Putero, Lucas Kreiman, Julian Mensh
Vieira Lab : Jorge Vieira, Cristina P. Vieira, Pedro Duque, Tânia Dias
d_CCRT <- read.csv("MasterSheets_Oct21_git/CCRT_MasterSheet_Oct21.csv")
str(d_CCRT)
## 'data.frame': 2016 obs. of 15 variables:
## $ Supervisor.PI : chr "Vieira" "Vieira" "Vieira" "Vieira" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 4 4 4 4 4 4 4 4 4 4 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : chr "Vieira_4_AK1_F_1" "Vieira_4_AK1_F_1" "Vieira_4_AK1_F_1" "Vieira_4_AK1_F_1" ...
## $ Individual : int 1 2 3 4 5 6 1 2 3 4 ...
## $ CCRT_seconds : int 2381 1902 1847 1640 2202 1444 1550 1900 1505 1524 ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_CCRT$Supervisor.PI <- as.factor(d_CCRT$Supervisor.PI)
d_CCRT$Diet <- as.factor(d_CCRT$Diet)
d_CCRT$Batch <- as.factor(d_CCRT$Batch)
d_CCRT$Population_Lat <- factor(d_CCRT$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_CCRT$Population_Lon <- factor(d_CCRT$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_CCRT$Population_Alt <- factor(d_CCRT$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_CCRT$Line <- as.factor(d_CCRT$Line)
d_CCRT$Sex <- as.factor(d_CCRT$Sex)
d_CCRT$ReplicateVial <- as.factor(d_CCRT$ReplicateVial)
d_CCRT$CCRT_seconds <- as.numeric(d_CCRT$CCRT_seconds)
d_CCRT$Censor <- as.numeric(d_CCRT$Censor)
str(d_CCRT)
## 'data.frame': 2016 obs. of 18 variables:
## $ Supervisor.PI : Factor w/ 1 level "Vieira": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 27 levels "1","2","3","4",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 168 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 2 2 2 2 ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : Factor w/ 336 levels "Vieira_1_UM13_F_1",..: 235 235 235 235 235 235 236 236 236 236 ...
## $ Individual : int 1 2 3 4 5 6 1 2 3 4 ...
## $ CCRT_seconds : num 2381 1902 1847 1640 2202 ...
## $ Censor : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_CCRT_F <-subset(d_CCRT,Sex=='F')
d_CCRT_M <-subset(d_CCRT,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_CCRT <- min(d_CCRT$CCRT_seconds)
max_CCRT <- max(d_CCRT$CCRT_seconds)
y-axis is scaled by the minimum (0) and maximum (1) values in the full data set.
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(CCRT_F_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 5461681 682710 8 39.995 3.7236 0.002453 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CCRT_F_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CCRT_seconds ~ Population + (1 | Population:Line) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: filter(d_CCRT_F, Censor == "0" & Supervisor.PI == "Vieira")
##
## REML criterion at convergence: 14874.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1461 -0.5789 -0.1437 0.3652 5.0319
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 799.8 28.28
## Population:Line (Intercept) 29469.4 171.67
## Batch (Intercept) 3852.3 62.07
## Residual 183347.9 428.19
## Number of obs: 993, groups:
## ReplicateVial:Line, 168; Population:Line, 168; Batch, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1582.02 62.96 38.07 25.126 < 2e-16 ***
## PopulationGI -55.11 90.30 158.62 -0.610 0.54250
## PopulationKA -193.88 88.41 40.42 -2.193 0.03413 *
## PopulationMA -287.19 89.91 30.12 -3.194 0.00328 **
## PopulationMU -49.61 91.16 26.95 -0.544 0.59079
## PopulationRE -147.82 88.37 115.37 -1.673 0.09711 .
## PopulationUM 131.98 89.75 41.13 1.470 0.14905
## PopulationVA -180.46 89.51 33.30 -2.016 0.05193 .
## PopulationYE -100.02 88.28 37.67 -1.133 0.26438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.573
## PopulatinKA -0.712 0.408
## PopulatinMA -0.700 0.401 0.508
## PopulatinMU -0.686 0.416 0.489 0.481
## PopulatinRE -0.656 0.391 0.468 0.459 0.451
## PopulatinUM -0.698 0.405 0.515 0.489 0.488 0.485
## PopulatinVA -0.703 0.403 0.501 0.493 0.483 0.461 0.491
## PopulatinYE -0.713 0.408 0.508 0.499 0.489 0.468 0.498 0.573
#could simplify the model, as some random factors explain very little
## boundary (singular) fit: see ?isSingular
## Warning: Model failed to converge with 1 negative eigenvalue: -4.2e-05
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(CCRT_M_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 6454279 806785 8 159.24 5.132 1.058e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(CCRT_M_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CCRT_seconds ~ Population + (1 | Population:Line) + (1 | Batch) +
## (1 | ReplicateVial:Line)
## Data: filter(d_CCRT_M, Censor == "0" & Supervisor.PI == "Vieira")
##
## REML criterion at convergence: 14768.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3458 -0.5562 -0.1358 0.3737 4.4516
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line (Intercept) 4.448e+04 2.109e+02
## Population:Line (Intercept) 1.539e+03 3.923e+01
## Batch (Intercept) 8.020e-05 8.955e-03
## Residual 1.572e+05 3.965e+02
## Number of obs: 993, groups:
## ReplicateVial:Line, 168; Population:Line, 168; Batch, 27
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1563.5083 60.0926 157.5243 26.018 < 2e-16 ***
## PopulationGI 281.3506 95.7428 157.5244 2.939 0.00379 **
## PopulationKA -117.8561 85.2747 159.5721 -1.382 0.16888
## PopulationMA -233.8839 85.0558 158.0250 -2.750 0.00666 **
## PopulationMU 130.9000 84.9838 157.5242 1.540 0.12550
## PopulationRE 0.1418 90.2451 158.2211 0.002 0.99875
## PopulationUM 14.8551 86.2529 158.6143 0.172 0.86348
## PopulationVA -58.1077 85.1283 158.5331 -0.683 0.49586
## PopulationYE 89.6352 85.3485 160.1033 1.050 0.29520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.628
## PopulatinKA -0.705 0.442
## PopulatinMA -0.707 0.443 0.498
## PopulatinMU -0.707 0.444 0.498 0.500
## PopulatinRE -0.666 0.418 0.469 0.470 0.471
## PopulatinUM -0.697 0.437 0.491 0.492 0.493 0.464
## PopulatinVA -0.706 0.443 0.497 0.499 0.499 0.470 0.492
## PopulatinYE -0.704 0.442 0.496 0.497 0.498 0.469 0.491 0.497
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#model needs simplification
#Please refer to "_SurvivalAnalyses_" folder
NA
For a detailed description of tables, plots, linear models and outputs, please refer here
Parsch Lab : Eliza Argyridou, Amanda Glaser-Schmitt, John Parsch
Vieira Lab : Jorge Vieira, Cristina P. Vieira, Pedro Duque, Tânia Dias
d_HSM <- read.csv("MasterSheets_Oct21_git/HSM_MasterSheet_Oct21.csv")
str(d_HSM)
## 'data.frame': 31244 obs. of 15 variables:
## $ Supervisor.PI : chr "Vieira" "Vieira" "Vieira" "Vieira" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : chr "Vieira_5_AK1_F_1" "Vieira_5_AK1_F_1" "Vieira_5_AK1_F_1" "Vieira_5_AK1_F_1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ TimeDeath_min : int 240 240 300 300 300 330 360 360 360 390 ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_HSM$Supervisor.PI <- as.factor(d_HSM$Supervisor.PI)
d_HSM$Diet <- as.factor(d_HSM$Diet)
d_HSM$Batch <- as.factor(d_HSM$Batch)
d_HSM$Population_Lat <- factor(d_HSM$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_HSM$Population_Lon <- factor(d_HSM$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_HSM$Population_Alt <- factor(d_HSM$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_HSM$Line <- as.factor(d_HSM$Line)
d_HSM$Sex <- as.factor(d_HSM$Sex)
d_HSM$ReplicateVial <- as.factor(d_HSM$ReplicateVial)
d_HSM$TimeDeath_min <- as.numeric(d_HSM$TimeDeath_min)
d_HSM$Censor <- as.numeric(d_HSM$Censor)
str(d_HSM)
## 'data.frame': 31244 obs. of 18 variables:
## $ Supervisor.PI : Factor w/ 2 levels "Parsch","Vieira": 2 2 2 2 2 2 2 2 2 2 ...
## $ Diet : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
## $ Batch : Factor w/ 32 levels "1","2","3","4",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : Factor w/ 1816 levels "Parsch_1_AK1_F_1",..: 1691 1691 1691 1691 1691 1691 1691 1691 1691 1691 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ TimeDeath_min : num 240 240 300 300 300 330 360 360 360 390 ...
## $ Censor : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_HSM_F <-subset(d_HSM,Sex=='F')
d_HSM_M <-subset(d_HSM,Sex=='M')
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
min_HSM <- min(d_HSM$TimeDeath_min)
max_HSM <- max(d_HSM$TimeDeath_min)
y-axis is scaled by the minimum (30) and maximum (505) values in the full data set.
anova(HSM_F_lmer_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 348447 43556 8 141.62 7.2493 4.868e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_F_lmer_Parsch)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: filter(d_HSM_F, Censor == "0" & Supervisor.PI == "Parsch")
##
## REML criterion at convergence: 39308.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9904 -0.5935 0.1317 0.6680 2.9181
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 2411 49.11
## Batch (Intercept) 3662 60.51
## Residual 6008 77.51
## Number of obs: 3384, groups: Line:Population, 133; Batch, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 394.1080 25.0877 17.5868 15.709 8.64e-12 ***
## PopulationGI -56.6034 20.0704 136.3060 -2.820 0.005515 **
## PopulationKA -24.4190 20.1169 136.0422 -1.214 0.226906
## PopulationMA 27.8126 20.7791 142.1194 1.338 0.182873
## PopulationMU 6.0355 19.9796 131.8722 0.302 0.763065
## PopulationRE -70.8761 20.6894 149.0449 -3.426 0.000792 ***
## PopulationUM 0.9422 22.0588 180.8989 0.043 0.965978
## PopulationVA -9.1233 22.1012 183.0276 -0.413 0.680238
## PopulationYE -81.9769 20.3008 140.3281 -4.038 8.83e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.428
## PopulatinKA -0.426 0.554
## PopulatinMA -0.408 0.493 0.501
## PopulatinMU -0.423 0.511 0.518 0.523
## PopulatinRE -0.445 0.519 0.518 0.498 0.515
## PopulatinUM -0.427 0.494 0.490 0.465 0.481 0.579
## PopulatinVA -0.426 0.492 0.488 0.464 0.480 0.577 0.572
## PopulatinYE -0.448 0.534 0.529 0.503 0.522 0.576 0.561 0.559
anova(HSM_M_lmer_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 319547 39943 8 134.26 8.2397 4.875e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_M_lmer_Parsch)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: filter(d_HSM_M, Censor == "0" & Supervisor.PI == "Parsch")
##
## REML criterion at convergence: 57794.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2106 -0.6839 0.0461 0.6733 2.8035
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 4376 66.15
## Batch (Intercept) 3268 57.17
## Residual 4848 69.63
## Number of obs: 5065, groups: Line:Population, 135; Batch, 9
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 382.380 25.995 25.149 14.710 7.34e-14 ***
## PopulationGI -97.318 24.844 131.718 -3.917 0.000143 ***
## PopulationKA -42.710 24.746 129.653 -1.726 0.086736 .
## PopulationMA 5.031 24.687 128.410 0.204 0.838830
## PopulationMU -30.094 24.625 127.191 -1.222 0.223939
## PopulationRE -119.245 25.215 139.083 -4.729 5.47e-06 ***
## PopulationUM -13.127 25.684 149.157 -0.511 0.610032
## PopulationVA -29.298 25.675 148.962 -1.141 0.255655
## PopulationYE -124.398 24.964 133.987 -4.983 1.90e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.478
## PopulatinKA -0.478 0.508
## PopulatinMA -0.474 0.492 0.497
## PopulatinMU -0.476 0.493 0.498 0.504
## PopulatinRE -0.487 0.494 0.494 0.490 0.492
## PopulatinUM -0.484 0.488 0.487 0.481 0.482 0.533
## PopulatinVA -0.484 0.488 0.487 0.481 0.482 0.533 0.540
## PopulatinYE -0.488 0.500 0.499 0.493 0.494 0.521 0.522 0.522
p_HSM_M_Parsch <- ggplot(data = filter(d_HSM_M, (Censor == "0") & (Supervisor.PI == "Parsch")), aes(x=Population_Lat, y=TimeDeath_min, fill=Batch)) +
geom_boxplot(outlier.shape = NA, notch=FALSE) +
labs(title="p_HSM_M_Parsch", x="Population", y = "TimeDeath_min") + ylim(c(min_HSM, max_HSM))+ droseu_theme
p_HSM_M_Parsch
pdf(file="HeatShock/p_HSM_M_Parsch.pdf",width=8, height=5)
p_HSM_M_Parsch
invisible(dev.off())
anova(HSM_F_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 167083 20885 8 51.73 6.8529 4.169e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_F_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: filter(d_HSM_F, Censor == "0" & Supervisor.PI == "Vieira")
##
## REML criterion at convergence: 57881.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2647 -0.5129 0.1397 0.7262 2.3668
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 433.75 20.827
## Batch (Intercept) 17.84 4.223
## Residual 3047.69 55.206
## Number of obs: 5310, groups: Line:Population, 168; Batch, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 362.7389 5.7290 72.7338 63.316 < 2e-16 ***
## PopulationGI -28.7426 8.5826 111.6208 -3.349 0.001107 **
## PopulationKA 1.1336 8.3189 72.8507 0.136 0.891986
## PopulationMA 9.7117 8.1535 54.1027 1.191 0.238812
## PopulationMU -30.8386 7.6764 111.6067 -4.017 0.000107 ***
## PopulationRE 14.4083 8.7199 49.0669 1.652 0.104849
## PopulationUM 1.8238 8.1387 80.5809 0.224 0.823259
## PopulationVA -5.3199 7.9537 45.2138 -0.669 0.506988
## PopulationYE -0.5634 8.0726 49.4488 -0.070 0.944641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.649
## PopulatinKA -0.688 0.447
## PopulatinMA -0.703 0.456 0.486
## PopulatinMU -0.711 0.475 0.490 0.499
## PopulatinRE -0.657 0.427 0.456 0.462 0.467
## PopulatinUM -0.698 0.459 0.496 0.491 0.507 0.459
## PopulatinVA -0.720 0.468 0.496 0.506 0.512 0.473 0.503
## PopulatinYE -0.710 0.461 0.489 0.502 0.504 0.466 0.495 0.540
anova(HSM_M_lmer_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 83890 10486 8 53.384 3.5201 0.002454 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(HSM_M_lmer_Vieira)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TimeDeath_min ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: filter(d_HSM_M, Censor == "0" & Supervisor.PI == "Vieira")
##
## REML criterion at convergence: 95195.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8393 -0.6019 0.0454 0.6858 2.7930
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 1002.0 31.65
## Batch (Intercept) 260.4 16.14
## Residual 2979.0 54.58
## Number of obs: 8743, groups: Line:Population, 168; Batch, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 343.597 9.407 61.170 36.527 < 2e-16 ***
## PopulationGI -66.412 13.504 130.651 -4.918 2.58e-06 ***
## PopulationKA -16.810 13.520 60.216 -1.243 0.21856
## PopulationMA -23.992 13.906 46.304 -1.725 0.09114 .
## PopulationMU -37.395 11.948 145.444 -3.130 0.00211 **
## PopulationRE -16.376 15.164 39.338 -1.080 0.28674
## PopulationUM -29.091 12.987 89.371 -2.240 0.02757 *
## PopulationVA -23.582 14.048 45.588 -1.679 0.10007
## PopulationYE -18.602 14.066 47.091 -1.322 0.19239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.575
## PopulatinKA -0.690 0.400
## PopulatinMA -0.676 0.389 0.485
## PopulatinMU -0.595 0.410 0.419 0.403
## PopulatinRE -0.620 0.357 0.455 0.420 0.370
## PopulatinUM -0.672 0.420 0.526 0.456 0.470 0.420
## PopulatinVA -0.670 0.385 0.462 0.459 0.399 0.415 0.450
## PopulatinYE -0.669 0.385 0.462 0.477 0.398 0.415 0.449 0.591
#Please refer to "_SurvivalAnalyses_" folder
There are 9 populations and 132 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Bergland Lab : Liam Miller, Alan Bergland, Priscilla Erickson
Flatt Lab : Esra Durmaz, Envel Kerdaffrec, Thibault Schowing, Virginie Thieu, Marisa Rodrigues, Thomas Flatt
Schlotterer Lab : Manolis Lyrakis, Christian Schlötterer
d_Dia <- read.csv("MasterSheets_Oct21_git/DIA_MasterSheet_Oct21.csv")
str(d_Dia)
## 'data.frame': 8206 obs. of 12 variables:
## $ Supervisor.PI : chr "Bergland" "Bergland" "Bergland" "Bergland" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ MostAdvancedStage: int 9 7 10 8 8 8 8 9 9 8 ...
## $ NumberOfEggs : int 1 0 3 0 1 3 0 4 3 1 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_Dia <- d_Dia %>%
mutate(Supervisor.PI = as.factor(Supervisor.PI),
Diet = as.factor(Diet),
Batch = as.factor(Batch),
Population = as.factor(Population),
Line = as.factor(Line),
MostAdvancedStage = as.numeric(MostAdvancedStage),
NumberOfEggs = as.numeric(NumberOfEggs),
Max_Stage7 = ifelse(MostAdvancedStage <= 7 & NumberOfEggs == 0, 1, 0),
Max_Stage8 = ifelse(MostAdvancedStage <= 8 & NumberOfEggs == 0, 1, 0),
Max_Stage9 = ifelse(MostAdvancedStage <= 9 & NumberOfEggs == 0, 1, 0),
Population_Lat = factor(Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK")),
Population_Lon = factor(Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA")),
Population_Alt = factor(Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE")),
Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Altitude = as.numeric(Altitude))
str(d_Dia)
## 'data.frame': 8206 obs. of 18 variables:
## $ Supervisor.PI : Factor w/ 3 levels "Bergland","Flatt",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
## $ Batch : Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : Factor w/ 168 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ MostAdvancedStage: num 9 7 10 8 8 8 8 9 9 8 ...
## $ NumberOfEggs : num 1 0 3 0 1 3 0 4 3 1 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : num 88 88 88 88 88 88 88 88 88 88 ...
## $ Max_Stage7 : num 0 1 0 0 0 0 0 0 0 0 ...
## $ Max_Stage8 : num 0 1 0 1 0 0 1 0 0 0 ...
## $ Max_Stage9 : num 0 1 0 1 0 0 1 0 0 0 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
##### calculate diapause proportions per line, with and without batch
d_Dia_Line_wbatch <- d_Dia %>%
group_by(Supervisor.PI, Batch, Population, Population_Lat, Population_Lon, Population_Alt, Line) %>%
dplyr::summarise(n_ind = as.numeric(n()),
Max_Stage7_Prop = mean(Max_Stage7, na.rm = T),
Max_Stage8_Prop = mean(Max_Stage8, na.rm = T),
Max_Stage9_Prop = mean(Max_Stage9, na.rm = T),
Max_Stage7_Prop_Asin = asin(sqrt(mean(Max_Stage7, na.rm = T))),
Max_Stage8_Prop_Asin = asin(sqrt(mean(Max_Stage8, na.rm = T))),
Max_Stage9_Prop_Asin = asin(sqrt(mean(Max_Stage9, na.rm = T))),
.groups = "drop")
d_Dia_Line_wobatch <- d_Dia %>%
group_by(Supervisor.PI, Population, Population_Lat, Population_Lon, Population_Alt, Line) %>%
dplyr::summarise(n_ind = as.numeric(n()),
Max_Stage7_Prop = mean(Max_Stage7, na.rm = T),
Max_Stage8_Prop = mean(Max_Stage8, na.rm = T),
Max_Stage9_Prop = mean(Max_Stage9, na.rm = T),
Max_Stage7_Prop_Asin = asin(sqrt(mean(Max_Stage7, na.rm = T))),
Max_Stage8_Prop_Asin = asin(sqrt(mean(Max_Stage8, na.rm = T))),
Max_Stage9_Prop_Asin = asin(sqrt(mean(Max_Stage9, na.rm = T))),
.groups = "drop")
Descriptive statistics at the line level for Max Stage 9, with batch information :
Descriptive statistics at the line level for Max Stage 9, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
## Joining, by = "Population"
## boundary (singular) fit: see ?isSingular
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(Dia_lmer_Bergland)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1.3521 0.16901 8 142.78 2.4502 0.01627 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Dia_lmer_Bergland)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Max_Stage9_Prop ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_Dia_trans %>% filter(Supervisor.PI == "Bergland")
##
## REML criterion at convergence: 120.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.65951 -0.55229 0.06555 0.60594 2.16115
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 0.03207 0.1791
## Batch (Intercept) 0.00000 0.0000
## Residual 0.06898 0.2626
## Number of obs: 189, groups: Population:Line, 158; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.83272 0.07493 171.53702 11.114 < 2e-16 ***
## PopulationGI 0.01514 0.10952 162.37619 0.138 0.89025
## PopulationKA 0.08145 0.10052 153.94814 0.810 0.41902
## PopulationMA 0.19121 0.10348 165.25148 1.848 0.06643 .
## PopulationMU 0.19054 0.10267 150.73567 1.856 0.06544 .
## PopulationRE 0.03330 0.11147 161.33108 0.299 0.76552
## PopulationUM 0.30637 0.10671 144.03589 2.871 0.00471 **
## PopulationVA 0.29892 0.10285 161.97413 2.906 0.00417 **
## PopulationYE 0.18925 0.10138 159.76296 1.867 0.06376 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.684
## PopulatinKA -0.745 0.510
## PopulatinMA -0.724 0.495 0.540
## PopulatinMU -0.730 0.499 0.544 0.528
## PopulatinRE -0.672 0.460 0.501 0.487 0.491
## PopulatinUM -0.702 0.480 0.523 0.508 0.512 0.472
## PopulatinVA -0.728 0.498 0.543 0.527 0.532 0.490 0.512
## PopulatinYE -0.739 0.506 0.551 0.535 0.539 0.497 0.519 0.538
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
min_Dia <- min(d_Dia_Line_wbatch$Max_Stage9_Prop)
max_Dia <- max(d_Dia_Line_wbatch$Max_Stage9_Prop)
## Joining, by = "Population"
anova(Dia_lm_Flatt)
## Analysis of Variance Table
##
## Response: Max_Stage9_Prop
## Df Sum Sq Mean Sq F value Pr(>F)
## Population 8 0.202 0.025252 0.7367 0.659
## Residuals 154 5.279 0.034279
summary(Dia_lm_Flatt)
##
## Call:
## lm(formula = Max_Stage9_Prop ~ Population, data = d_Dia_trans %>%
## filter(Supervisor.PI == "Flatt"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67844 -0.14098 0.07585 0.13055 0.15248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.431116 0.041400 34.568 <2e-16 ***
## PopulationGI 0.009134 0.064517 0.142 0.888
## PopulationKA 0.063826 0.058548 1.090 0.277
## PopulationMA 0.002850 0.059314 0.048 0.962
## PopulationMU 0.077286 0.058548 1.320 0.189
## PopulationRE 0.019845 0.064517 0.308 0.759
## PopulationUM 0.090335 0.062100 1.455 0.148
## PopulationVA -0.012795 0.058548 -0.219 0.827
## PopulationYE 0.027647 0.058548 0.472 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1851 on 154 degrees of freedom
## Multiple R-squared: 0.03686, Adjusted R-squared: -0.01318
## F-statistic: 0.7367 on 8 and 154 DF, p-value: 0.659
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
anova(Dia_lmer_Schlotterer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.8957 0.11196 8 150.12 2.3476 0.0209 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Dia_lmer_Schlotterer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Max_Stage9_Prop ~ Population + (1 | Population:Line) + (1 | Batch)
## Data: d_Dia_trans %>% filter(Supervisor.PI == "Schlotterer")
##
## REML criterion at convergence: 122.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.11795 -0.50145 -0.00727 0.51676 1.62781
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 0.05690 0.23854
## Batch (Intercept) 0.00464 0.06812
## Residual 0.04769 0.21839
## Number of obs: 195, groups: Population:Line, 159; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.81061 0.08059 19.57204 10.059 3.56e-09 ***
## PopulationGI 0.14593 0.10778 147.49028 1.354 0.1778
## PopulationKA 0.21820 0.09981 149.17906 2.186 0.0304 *
## PopulationMA 0.22945 0.10034 144.10264 2.287 0.0237 *
## PopulationMU 0.24726 0.09999 149.56005 2.473 0.0145 *
## PopulationRE 0.21480 0.10889 155.57976 1.973 0.0503 .
## PopulationUM 0.45234 0.10879 153.63777 4.158 5.32e-05 ***
## PopulationVA 0.25405 0.10645 152.79899 2.387 0.0182 *
## PopulationYE 0.18575 0.10086 142.57733 1.842 0.0676 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.570
## PopulatinKA -0.615 0.462
## PopulatinMA -0.612 0.458 0.494
## PopulatinMU -0.615 0.460 0.496 0.495
## PopulatinRE -0.566 0.426 0.457 0.455 0.459
## PopulatinUM -0.564 0.415 0.453 0.454 0.456 0.416
## PopulatinVA -0.578 0.432 0.466 0.464 0.468 0.430 0.428
## PopulatinYE -0.612 0.461 0.494 0.493 0.499 0.461 0.452 0.467
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
There are 9 populations and 146 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
#Note that the trait has been phenotyped as mixed sex.
Wegener Lab : Susanne Klühspies, Christian Wegener
d_CET <- read.csv("MasterSheets_Oct21_git/CET_MasterSheet_Dec21.csv")
str(d_CET)
## 'data.frame': 36 obs. of 16 variables:
## $ Supervisor.PI : chr "Wegener" "Wegener" "Wegener" "Wegener" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ total...of.flies : int 1897 793 1938 1355 2349 1418 1178 1544 2150 1236 ...
## $ Batch : int 2 2 2 2 3 2 1 2 3 2 ...
## $ Population : chr "AK" "GI" "KA" "MA" ...
## $ Line : chr "mixed population" "mixed population" "mixed population" "mixed population" ...
## $ Sex : chr "mixed sex" "mixed sex" "mixed sex" "mixed sex" ...
## $ ReplicateVial : logi NA NA NA NA NA NA ...
## $ Individual : logi NA NA NA NA NA NA ...
## $ Condition : chr "18_LD_DD" "18_LD_DD" "18_LD_DD" "18_LD_DD" ...
## $ CET_hours_MESA : num 13.2 10.5 14 16.8 10.5 ...
## $ CET_hours_LSPR : num 12.5 10.5 11.7 17.5 11 ...
## $ Period_MESA : num 24.2 24.8 24.1 23.3 24.9 ...
## $ Period_LSPR : num 24.4 24.8 24.8 23.1 24.7 ...
## $ Rhythmicity_LSPR_amplitude : num 11.66 6.55 9.75 7.47 16.88 ...
## $ Rhythmicity_JTK_p_BH_corrected: num 5.26e-24 6.27e-22 6.76e-14 2.92e-09 4.21e-24 ...
d_CET$Supervisor.PI <- as.factor(d_CET$Supervisor.PI)
d_CET$Diet <- as.factor(d_CET$Diet)
d_CET$Batch <- as.factor(d_CET$Batch)
d_CET$Population_Lat <- factor(d_CET$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_CET$Population_Lon <- factor(d_CET$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_CET$Population_Alt <- factor(d_CET$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_CET$Condition <- as.factor(d_CET$Condition)
d_CET$CET_hours_MESA <- as.numeric(d_CET$CET_hours_MESA)
d_CET$CET_hours_LSPR <- as.numeric(d_CET$CET_hours_LSPR)
d_CET$Period_MESA <- as.numeric(d_CET$Period_MESA)
d_CET$Period_LSPR <- as.numeric(d_CET$Period_LSPR)
d_CET$Rhythmicity_LSPR_amplitude <- as.numeric(d_CET$Rhythmicity_LSPR_amplitude)
d_CET$Rhythmicity_JTK_p_BH_corrected <- as.numeric(d_CET$Rhythmicity_JTK_p_BH_corrected)
str(d_CET)
## 'data.frame': 36 obs. of 19 variables:
## $ Supervisor.PI : Factor w/ 1 level "Wegener": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ total...of.flies : int 1897 793 1938 1355 2349 1418 1178 1544 2150 1236 ...
## $ Batch : Factor w/ 4 levels "1","2","3","4": 2 2 2 2 3 2 1 2 3 2 ...
## $ Population : chr "AK" "GI" "KA" "MA" ...
## $ Line : chr "mixed population" "mixed population" "mixed population" "mixed population" ...
## $ Sex : chr "mixed sex" "mixed sex" "mixed sex" "mixed sex" ...
## $ ReplicateVial : logi NA NA NA NA NA NA ...
## $ Individual : logi NA NA NA NA NA NA ...
## $ Condition : Factor w/ 4 levels "18_LD_DD","18_LD_LD",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ CET_hours_MESA : num 13.2 10.5 14 16.8 10.5 ...
## $ CET_hours_LSPR : num 12.5 10.5 11.7 17.5 11 ...
## $ Period_MESA : num 24.2 24.8 24.1 23.3 24.9 ...
## $ Period_LSPR : num 24.4 24.8 24.8 23.1 24.7 ...
## $ Rhythmicity_LSPR_amplitude : num 11.66 6.55 9.75 7.47 16.88 ...
## $ Rhythmicity_JTK_p_BH_corrected: num 5.26e-24 6.27e-22 6.76e-14 2.92e-09 4.21e-24 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 3 7 5 4 2 6 8 1 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 2 3 5 4 1 7 9 8 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 3 1 8 7 4 5 6 9 2 ...
d_CET_18_LD_DD <-subset(d_CET,Condition=='18_LD_DD')
d_CET_18_LD_LD <-subset(d_CET,Condition=='18_LD_LD')
d_CET_29_LD_DD <-subset(d_CET,Condition=='29_LD_DD')
d_CET_29_LD_LD <-subset(d_CET,Condition=='29_LD_LD')
Descriptive statistics for CET_hours_MESA :
Descriptive statistics for CET_hours_LSPR :
Descriptive statistics for Period_MESA :
Descriptive statistics for Period_LSPR :
Descriptive statistics for Rhythmicity_LSPR_amplitude :
Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :
Descriptive statistics for CET_hours_MESA :
Descriptive statistics for CET_hours_LSPR :
Descriptive statistics for Period_MESA :
Descriptive statistics for Period_LSPR :
Descriptive statistics for Rhythmicity_LSPR_amplitude :
Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :
Descriptive statistics for CET_hours_MESA :
Descriptive statistics for CET_hours_LSPR :
Descriptive statistics for Period_MESA :
Descriptive statistics for Period_LSPR :
Descriptive statistics for Rhythmicity_LSPR_amplitude :
Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :
table_CET_29LDDD_Rhy_JTK <- write.csv(d_CET_29_LD_DD %>% group_by(Supervisor.PI, Condition, Population) %>%
summarise_at(vars(Rhythmicity_JTK_p_BH_corrected),
list(Mean = mean)),
file = "CircadianEclosion/table_CET_29LDDD_Rhy_JTK.csv", row.names = T)
table_CET_29LDDD_Rhy_JTK <- read.csv("CircadianEclosion/table_CET_29LDDD_Rhy_JTK.csv")
DT::datatable(table_CET_29LDDD_Rhy_JTK, options = list(pageLength = 10)) %>% formatRound(5:5, 3)
Descriptive statistics for CET_hours_MESA :
Descriptive statistics for CET_hours_LSPR :
Descriptive statistics for Period_MESA :
Descriptive statistics for Period_LSPR :
Descriptive statistics for Rhythmicity_LSPR_amplitude :
Descriptive statistics for Rhythmicity_JTK_p_BH_corrected :
From Chris :
From Eran :
knitr::include_graphics("CircadianEclosion/FromEran/eclosion_data_Christian_Page_1.png")
knitr::include_graphics("CircadianEclosion/FromEran/eclosion_data_Christian_Page_2.png")
For a detailed description of tables, plots, linear models and outputs, please refer here
#Note that the trait has been phenotyped only in males.
Tauber Lab : Bettina Fishman, Eran Tauber
d_LA <- read.csv("MasterSheets_Oct21_git/LA_MasterSheet_Oct21.csv")
str(d_LA)
## 'data.frame': 639 obs. of 19 variables:
## $ Supervisor.PI : chr "Tauber" "Tauber" "Tauber" "Tauber" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "YE" "YE" "YE" "YE" ...
## $ Line : chr "YE27" "YE33" "YE40" "YE40" ...
## $ Data.Label : chr "YE 29-9-18" "YE 33-9-18" "YE 40-9-18" "YE 40-9-18" ...
## $ Sex : chr "M" "M" "M" "M" ...
## $ ReplicateChamberOld: int 3 3 3 3 3 3 3 3 3 3 ...
## $ ReplicateChamber : chr "Tauber_1_YE27_M_3" "Tauber_1_YE33_M_3" "Tauber_1_YE40_M_3" "Tauber_1_YE40_M_3" ...
## $ Period : num 23.6 20.2 24 24.2 23.1 ...
## $ CircPhase : num 23.21 13.07 4.59 9.32 12.98 ...
## $ AbsPhase : num 12.5 0.7 11.7 12.5 12.5 12 12.7 0.5 12.4 12.6 ...
## $ Experiment : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ND : num 0.678 0.95 1.083 0.948 1.455 ...
## $ Activity : num 29.2 46.8 45.4 37.8 37.8 31.8 43.6 42.8 29.6 21.6 ...
## $ Country : chr "Turkey" "Turkey" "Turkey" "Turkey" ...
## $ Latitude : num 40.2 40.2 40.2 40.2 40.2 ...
## $ Longitude : num 32.3 32.3 32.3 32.3 32.3 ...
## $ Altitude : int 680 680 680 680 680 680 680 680 680 680 ...
d_LA$Supervisor.PI <- as.factor(d_LA$Supervisor.PI)
d_LA$Diet <- as.factor(d_LA$Diet)
d_LA$Batch <- as.factor(d_LA$Batch)
d_LA$Population_Lat <- factor(d_LA$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_LA$Population_Lon <- factor(d_LA$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_LA$Population_Alt <- factor(d_LA$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_LA$Line <- as.factor(d_LA$Line)
d_LA$ReplicateChamber <- as.factor(d_LA$ReplicateChamber)
d_LA$Period <- as.numeric(d_LA$Period)
d_LA$CircPhase <- as.numeric(d_LA$CircPhase)
d_LA$AbsPhase <- as.numeric(d_LA$AbsPhase)
d_LA$Experiment <- as.numeric(d_LA$Experiment)
d_LA$ND <- as.numeric(d_LA$ND)
d_LA$Activity <- as.numeric(d_LA$Activity)
str(d_LA)
## 'data.frame': 639 obs. of 22 variables:
## $ Supervisor.PI : Factor w/ 1 level "Tauber": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "YE" "YE" "YE" "YE" ...
## $ Line : Factor w/ 92 levels "AK1","AK2","AK3",..: 84 85 86 86 86 89 90 90 91 91 ...
## $ Data.Label : chr "YE 29-9-18" "YE 33-9-18" "YE 40-9-18" "YE 40-9-18" ...
## $ Sex : chr "M" "M" "M" "M" ...
## $ ReplicateChamberOld: int 3 3 3 3 3 3 3 3 3 3 ...
## $ ReplicateChamber : Factor w/ 152 levels "Tauber_1_AK1_M_9",..: 76 77 78 78 78 79 80 80 81 81 ...
## $ Period : num 23.6 20.2 24 24.2 23.1 ...
## $ CircPhase : num 23.21 13.07 4.59 9.32 12.98 ...
## $ AbsPhase : num 12.5 0.7 11.7 12.5 12.5 12 12.7 0.5 12.4 12.6 ...
## $ Experiment : num 1 1 1 1 1 1 1 1 1 1 ...
## $ ND : num 0.678 0.95 1.083 0.948 1.455 ...
## $ Activity : num 29.2 46.8 45.4 37.8 37.8 31.8 43.6 42.8 29.6 21.6 ...
## $ Country : chr "Turkey" "Turkey" "Turkey" "Turkey" ...
## $ Latitude : num 40.2 40.2 40.2 40.2 40.2 ...
## $ Longitude : num 32.3 32.3 32.3 32.3 32.3 ...
## $ Altitude : int 680 680 680 680 680 680 680 680 680 680 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 8 8 8 8 8 8 8 8 8 8 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
table_LA_ND_Pop_wobatch <- write.csv(d_LA %>% group_by(Supervisor.PI, Sex, Population) %>%
summarise_at(vars(ND),
list(Mean = mean, SD= sd, Median = median, Min = min, Max = max,
SE = std_err, CV = coef_var, Mode = estimate_mode)),
file = "Locomotor/table_LA_ND_Pop_wobatch.csv", row.names = T)
table_LA_ND_Pop_wobatch <- read.csv("Locomotor/table_LA_ND_Pop_wobatch.csv")
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00214269 (tol = 0.002, component 1)
anova(LA_lmer_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1.8666 0.23332 8 74.993 1.5771 0.1461
summary(LA_lmer_ND_Tauber)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ND ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: d_LA
##
## REML criterion at convergence: 703.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1922 -0.4257 -0.0668 0.2481 11.4146
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 3.972e-02 0.1993100
## Batch (Intercept) 1.608e-09 0.0000401
## Residual 1.479e-01 0.3846341
## Number of obs: 639, groups: Line:Population, 92; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.86109 0.08409 63.09959 10.240 4.83e-15 ***
## PopulationGI -0.00947 0.12019 69.05962 -0.079 0.9374
## PopulationKA 0.16250 0.11441 64.97528 1.420 0.1603
## PopulationMA 0.09686 0.11425 65.05487 0.848 0.3997
## PopulationMU 0.00468 0.12120 77.61572 0.039 0.9693
## PopulationRE 0.01998 0.12567 74.00461 0.159 0.8741
## PopulationUM -0.05699 0.11757 66.31573 -0.485 0.6295
## PopulationVA 0.04927 0.11799 64.71625 0.418 0.6777
## PopulationYE 0.22780 0.10520 73.45997 2.165 0.0336 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.700
## PopulatinKA -0.735 0.514
## PopulatinMA -0.736 0.515 0.541
## PopulatinMU -0.694 0.485 0.510 0.511
## PopulatinRE -0.669 0.468 0.492 0.493 0.464
## PopulatinUM -0.715 0.500 0.526 0.526 0.496 0.479
## PopulatinVA -0.713 0.499 0.524 0.525 0.494 0.477 0.510
## PopulatinYE -0.799 0.559 0.587 0.588 0.555 0.535 0.572 0.570
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00214269 (tol = 0.002, component 1)
CircPhase
anova(LA_lmer_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1267.7 158.47 8 57.85 3.4025 0.002873 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LA_lmer_CircPhase_Tauber)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CircPhase ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: (d_LA)
##
## REML criterion at convergence: 4247.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.10384 -0.57625 -0.06697 0.85374 1.83691
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.04096 0.2024
## Batch (Intercept) 0.41358 0.6431
## Residual 46.57281 6.8244
## Number of obs: 639, groups: Line:Population, 92; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.0667 0.9362 8.2604 9.684 8.62e-06 ***
## PopulationGI 4.1447 1.1992 59.2979 3.456 0.001019 **
## PopulationKA 4.9461 1.1019 45.7989 4.489 4.81e-05 ***
## PopulationMA 3.8907 1.1176 52.8628 3.481 0.001009 **
## PopulationMU 3.1054 1.2721 79.9386 2.441 0.016851 *
## PopulationRE 4.2240 1.3234 66.9784 3.192 0.002153 **
## PopulationUM 1.9322 1.1559 53.6304 1.672 0.100412
## PopulationVA 3.9101 1.1388 47.0096 3.433 0.001254 **
## PopulationYE 3.8560 1.0930 73.7527 3.528 0.000725 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.596
## PopulatinKA -0.651 0.508
## PopulatinMA -0.645 0.503 0.550
## PopulatinMU -0.558 0.436 0.475 0.469
## PopulatinRE -0.559 0.435 0.479 0.481 0.400
## PopulatinUM -0.621 0.484 0.529 0.525 0.453 0.458
## PopulatinVA -0.634 0.494 0.541 0.538 0.461 0.474 0.516
## PopulatinYE -0.660 0.515 0.564 0.560 0.480 0.493 0.538 0.551
Activity
anova(LA_lmer_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 2149.2 268.65 8 71.722 3.8883 0.0007429 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(LA_lmer_Activity_Tauber)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Activity ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: (d_LA)
##
## REML criterion at convergence: 4624.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.16030 -0.55380 0.07697 0.65610 2.31656
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 41.72 6.459
## Batch (Intercept) 7.27 2.696
## Residual 69.09 8.312
## Number of obs: 639, groups: Line:Population, 92; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 31.197 3.140 6.396 9.935 4.02e-05 ***
## PopulationGI 3.118 3.518 67.155 0.886 0.3786
## PopulationKA 4.583 3.376 64.374 1.358 0.1793
## PopulationMA 5.644 3.376 64.461 1.672 0.0994 .
## PopulationMU -6.072 3.498 72.038 -1.736 0.0869 .
## PopulationRE 7.024 3.688 72.820 1.905 0.0608 .
## PopulationUM -5.237 3.465 64.916 -1.512 0.1355
## PopulationVA -2.690 3.486 65.345 -0.772 0.4431
## PopulationYE 3.049 3.068 69.746 0.994 0.3237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.563
## PopulatinKA -0.587 0.524
## PopulatinMA -0.588 0.525 0.547
## PopulatinMU -0.566 0.505 0.526 0.527
## PopulatinRE -0.543 0.484 0.505 0.508 0.484
## PopulatinUM -0.573 0.511 0.533 0.534 0.513 0.494
## PopulatinVA -0.570 0.509 0.530 0.532 0.510 0.494 0.518
## PopulatinYE -0.648 0.578 0.603 0.604 0.580 0.561 0.588 0.586
From Eran :
Red letters denote significant difference by Multiple Comparisons of Means: Tukey Contrasts
knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_1.png")
knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_2.png")
knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_3.png")
Phase in DD represented in angle. The mean and 95 confidence limits are shown. CI limits in red indicate uniform distribution (low concentration. i.e. Rayleigh test not significant).
knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_4.png")
Oriana 3 (Stacked circular).
knitr::include_graphics("Locomotor/FromEran/pheno2022_Page_5.png")
For a detailed description of tables, plots, linear models and outputs, please refer here
Gonzalez Lab : Llewellyn Green, Josefa Gonzalez, Miriam Merenciano
Onder Lab : Seda Coskun, Ekin Demir, Senel Selin Senkal, Cansu Aksoy
Pasyukova Lab : Alexander Symonenko, Natalia Roshina, Mikhail Trostnokov, Ekaterina Veselkina, Evgenia Tsybul’ko, Olga Rybina, Elena Pasyukova
d_SR <- read.csv("MasterSheets_Oct21_git/SR_MasterSheet_Nov21.csv")
str(d_SR)
## 'data.frame': 78315 obs. of 14 variables:
## $ Supervisor.PI : chr "Gonzalez" "Gonzalez" "Gonzalez" "Gonzalez" ...
## $ Diet : chr "S" "S" "S" "S" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : chr "F" "F" "F" "F" ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : chr "Gonzalez_1_AK1_F_1" "Gonzalez_1_AK1_F_1" "Gonzalez_1_AK1_F_1" "Gonzalez_1_AK1_F_1" ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ AgeAtDeath_hours: int 56 96 96 96 104 104 104 112 112 120 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_SR$Supervisor.PI <- as.factor(d_SR$Supervisor.PI)
d_SR$Diet <- as.factor(d_SR$Diet)
d_SR$Batch <- as.factor(d_SR$Batch)
d_SR$Population_Lat <- factor(d_SR$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_SR$Population_Lon <- factor(d_SR$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_SR$Population_Alt <- factor(d_SR$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_SR$Line <- as.factor(d_SR$Line)
d_SR$Sex <- as.factor(d_SR$Sex)
d_SR$ReplicateVial <- as.factor(d_SR$ReplicateVial)
d_SR$AgeAtDeath_hours <- as.numeric(d_SR$AgeAtDeath_hours)
str(d_SR)
## 'data.frame': 78315 obs. of 17 variables:
## $ Supervisor.PI : Factor w/ 3 levels "Gonzalez","Onder",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 2 levels "NS","S": 2 2 2 2 2 2 2 2 2 2 ...
## $ Batch : Factor w/ 7 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 171 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVialOld: int 1 1 1 1 1 1 1 1 1 1 ...
## $ ReplicateVial : Factor w/ 7753 levels "Gonzalez_1_AK1_F_1",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 10 ...
## $ AgeAtDeath_hours: num 56 96 96 96 104 104 104 112 112 120 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_SR_F <-subset(d_SR,Sex=='F')
d_SR_M <-subset(d_SR,Sex=='M')
Descriptive statistics at the line level, with batch information :
table_SR_Line_wbatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Batch, Sex,Population, Line) %>%
summarise_at(vars(AgeAtDeath_hours),
list(Mean = mean, SD= sd, Median = median, Min = min, Max = max,
SE = std_err, CV = coef_var, Mode = estimate_mode)),
file = "Starvation/table_SR_Line_wbatch.csv", row.names = T)
table_SR_Line_wbatch <- read.csv("Starvation/table_SR_Line_wbatch.csv")
Descriptive statistics at the line level, without batch information :
table_SR_Line_wobatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Sex,Population, Line) %>%
summarise_at(vars(AgeAtDeath_hours),
list(Mean = mean, SD= sd, Median = median, Min = min, Max = max,
SE = std_err, CV = coef_var, Mode = estimate_mode)),
file = "Starvation/table_SR_Line_wobatch.csv", row.names = T)
table_SR_Line_wobatch <- read.csv("Starvation/table_SR_Line_wobatch.csv")
Descriptive statistics at the population level, with batch information :
table_SR_Pop_wbatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Batch, Sex, Population) %>%
summarise_at(vars(AgeAtDeath_hours),
list(Mean = mean, SD= sd, Median = median, Min = min, Max = max,
SE = std_err, CV = coef_var, Mode = estimate_mode)),
file = "Starvation/table_SR_Pop_wbatch.csv", row.names = T)
table_SR_Pop_wbatch <- read.csv("Starvation/table_SR_Pop_wbatch.csv")
Descriptive statistics at the population level, without batch information :
table_SR_Pop_wobatch <- write.csv(d_SR %>% group_by(Supervisor.PI, Sex, Population) %>%
summarise_at(vars(AgeAtDeath_hours),
list(Mean = mean, SD= sd, Median = median, Min = min, Max = max,
SE = std_err, CV = coef_var, Mode = estimate_mode)),
file = "Starvation/table_SR_Pop_wobatch.csv", row.names = T)
table_SR_Pop_wobatch <- read.csv("Starvation/table_SR_Pop_wobatch.csv")
min_SR <- min(d_SR$AgeAtDeath_hours)
max_SR <- max(d_SR$AgeAtDeath_hours)
anova(SR_F_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 9342.2 1167.8 8 134.99 3.4548 0.001186 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_F_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |
## Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_SR_F, Supervisor.PI == "Gonzalez"))
##
## REML criterion at convergence: 43270.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6657 -0.6316 -0.0456 0.5852 4.9362
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 117.17 10.824
## Line:Population (Intercept) 126.21 11.235
## Batch (Intercept) 11.46 3.385
## Residual 338.01 18.385
## Number of obs: 4900, groups:
## ReplicateVial:Line:Population, 436; Line:Population, 141; Batch, 7
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 85.162 3.289 71.153 25.897 < 2e-16 ***
## PopulationGI -10.464 4.722 135.435 -2.216 0.028364 *
## PopulationKA -4.330 4.625 135.664 -0.936 0.350759
## PopulationMA -5.431 4.187 134.876 -1.297 0.196833
## PopulationMU 3.440 4.239 134.819 0.812 0.418497
## PopulationRE -5.408 4.736 136.434 -1.142 0.255514
## PopulationUM -3.445 4.937 131.240 -0.698 0.486603
## PopulationVA 3.236 4.421 132.504 0.732 0.465441
## PopulationYE -15.628 4.519 134.806 -3.458 0.000728 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.573
## PopulatinKA -0.591 0.404
## PopulatinMA -0.643 0.444 0.454
## PopulatinMU -0.621 0.435 0.442 0.491
## PopulatinRE -0.577 0.396 0.405 0.445 0.434
## PopulatinUM -0.540 0.375 0.383 0.423 0.417 0.375
## PopulatinVA -0.594 0.416 0.424 0.469 0.465 0.414 0.398
## PopulatinYE -0.578 0.406 0.413 0.459 0.455 0.404 0.390 0.436
anova(SR_M_lmer_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 6745.9 843.24 8 143.21 5.6436 3.145e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_M_lmer_Gonzalez)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |
## Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_SR_M, Supervisor.PI == "Gonzalez"))
##
## REML criterion at convergence: 42786.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8769 -0.6117 -0.0757 0.5087 8.1754
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 49.61 7.043
## Line:Population (Intercept) 60.30 7.766
## Batch (Intercept) 79.31 8.905
## Residual 149.42 12.224
## Number of obs: 5335, groups:
## ReplicateVial:Line:Population, 481; Line:Population, 156; Batch, 7
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.2899 3.9917 11.2314 14.853 9.84e-09 ***
## PopulationGI -8.2127 3.1208 143.3562 -2.632 0.009430 **
## PopulationKA -0.2066 3.1276 143.3321 -0.066 0.947426
## PopulationMA -5.3337 2.8943 143.1030 -1.843 0.067420 .
## PopulationMU 5.5319 2.9260 143.1123 1.891 0.060697 .
## PopulationRE -4.4639 3.1794 146.2780 -1.404 0.162434
## PopulationUM -1.9244 3.2338 140.5597 -0.595 0.552726
## PopulationVA -1.4989 2.8848 141.4527 -0.520 0.604152
## PopulationYE -11.5154 2.8977 143.1403 -3.974 0.000112 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.351
## PopulatinKA -0.356 0.443
## PopulatinMA -0.378 0.477 0.478
## PopulatinMU -0.362 0.467 0.464 0.503
## PopulatinRE -0.365 0.443 0.449 0.479 0.457
## PopulatinUM -0.332 0.424 0.423 0.458 0.452 0.420
## PopulatinVA -0.378 0.477 0.478 0.515 0.504 0.477 0.458
## PopulatinYE -0.381 0.477 0.479 0.515 0.502 0.481 0.457 0.515
## boundary (singular) fit: see ?isSingular
anova(SR_F_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 14650 1831.2 8 158.94 5.8393 1.537e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_F_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |
## Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_SR_F, Supervisor.PI == "Onder"))
##
## REML criterion at convergence: 149434.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0413 -0.6188 -0.0307 0.6086 3.9849
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 9.722 3.118
## Line:Population (Intercept) 168.283 12.972
## Batch (Intercept) 0.000 0.000
## Residual 313.603 17.709
## Number of obs: 17282, groups:
## ReplicateVial:Line:Population, 1737; Line:Population, 168; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 92.6510 2.9360 158.9928 31.557 < 2e-16 ***
## PopulationGI -4.4761 4.4849 159.0047 -0.998 0.31978
## PopulationKA 4.5913 4.1521 158.9897 1.106 0.27049
## PopulationMA -0.2778 4.1522 159.0007 -0.067 0.94675
## PopulationMU 10.1290 4.1512 158.8431 2.440 0.01579 *
## PopulationRE -3.8765 4.4033 158.8909 -0.880 0.37999
## PopulationUM 0.4304 4.3319 159.0573 0.099 0.92098
## PopulationVA 9.9510 4.1516 158.8990 2.397 0.01769 *
## PopulationYE -12.5121 4.1522 158.9921 -3.013 0.00301 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655
## PopulatinKA -0.707 0.463
## PopulatinMA -0.707 0.463 0.500
## PopulatinMU -0.707 0.463 0.500 0.500
## PopulatinRE -0.667 0.436 0.471 0.471 0.472
## PopulatinUM -0.678 0.444 0.479 0.479 0.479 0.452
## PopulatinVA -0.707 0.463 0.500 0.500 0.500 0.472 0.479
## PopulatinYE -0.707 0.463 0.500 0.500 0.500 0.471 0.479 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
#could simplufy the model, as some random factors explain very little
anova(SR_M_lmer_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 6159.6 769.95 8 158.28 4.128 0.0001689 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_M_lmer_Onder)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |
## Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_SR_M, Supervisor.PI == "Onder"))
##
## REML criterion at convergence: 139728.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1862 -0.6258 -0.0367 0.5970 5.2151
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 10.5531 3.2486
## Line:Population (Intercept) 95.9372 9.7948
## Batch (Intercept) 0.8989 0.9481
## Residual 186.5180 13.6572
## Number of obs: 17158, groups:
## ReplicateVial:Line:Population, 1732; Line:Population, 168; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 73.0727 2.2707 131.3319 32.181 <2e-16 ***
## PopulationGI -3.4277 3.4025 158.4759 -1.007 0.3153
## PopulationKA 2.3717 3.1545 159.2335 0.752 0.4533
## PopulationMA 0.1421 3.1452 157.4699 0.045 0.9640
## PopulationMU 6.7401 3.1446 157.3228 2.143 0.0336 *
## PopulationRE -3.0209 3.3370 157.6650 -0.905 0.3667
## PopulationUM 3.2483 3.2930 159.5561 0.986 0.3254
## PopulationVA 4.6345 3.1456 157.5723 1.473 0.1427
## PopulationYE -8.1979 3.1460 157.6663 -2.606 0.0100 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640
## PopulatinKA -0.696 0.463
## PopulatinMA -0.694 0.462 0.499
## PopulatinMU -0.693 0.461 0.498 0.500
## PopulatinRE -0.653 0.435 0.470 0.471 0.471
## PopulatinUM -0.667 0.443 0.483 0.479 0.477 0.450
## PopulatinVA -0.695 0.462 0.500 0.500 0.500 0.471 0.480
## PopulatinYE -0.694 0.462 0.501 0.500 0.500 0.471 0.480 0.500
anova(SR_F_lmer_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 17772 2221.5 8 159.75 5.9619 1.092e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_F_lmer_Pasyukova)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |
## Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_SR_F, Supervisor.PI == "Pasyukova"))
##
## REML criterion at convergence: 149510.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1750 -0.5760 0.0268 0.6125 3.5353
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 101.25 10.062
## Line:Population (Intercept) 113.34 10.646
## Batch (Intercept) 73.72 8.586
## Residual 372.62 19.303
## Number of obs: 16779, groups:
## ReplicateVial:Line:Population, 1681; Line:Population, 169; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 103.162 4.626 5.314 22.301 1.86e-06 ***
## PopulationGI 5.103 3.947 159.643 1.293 0.1979
## PopulationKA 1.952 3.486 159.684 0.560 0.5764
## PopulationMA -1.843 3.485 159.601 -0.529 0.5976
## PopulationMU 2.658 3.485 159.608 0.762 0.4469
## PopulationRE 0.366 3.706 159.554 0.099 0.9214
## PopulationUM 0.779 3.587 159.855 0.217 0.8283
## PopulationVA 7.520 3.486 159.633 2.158 0.0325 *
## PopulationYE -14.686 3.488 160.062 -4.211 4.24e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.321
## PopulatinKA -0.361 0.421
## PopulatinMA -0.359 0.421 0.476
## PopulatinMU -0.359 0.421 0.476 0.476
## PopulatinRE -0.338 0.396 0.448 0.448 0.448
## PopulatinUM -0.349 0.409 0.463 0.463 0.463 0.435
## PopulatinVA -0.359 0.421 0.476 0.476 0.476 0.448 0.463
## PopulatinYE -0.361 0.421 0.476 0.476 0.476 0.447 0.463 0.476
anova(SR_M_lmer_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 13147 1643.3 8 159.66 7.1587 4.403e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(SR_M_lmer_Pasyukova)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: AgeAtDeath_hours ~ Population + (1 | Line:Population) + (1 |
## Batch) + (1 | ReplicateVial:Line:Population)
## Data: (subset(d_SR_M, Supervisor.PI == "Pasyukova"))
##
## REML criterion at convergence: 141797.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5586 -0.6043 -0.0033 0.5733 5.7707
##
## Random effects:
## Groups Name Variance Std.Dev.
## ReplicateVial:Line:Population (Intercept) 49.00 7.000
## Line:Population (Intercept) 63.87 7.992
## Batch (Intercept) 11.53 3.395
## Residual 229.56 15.151
## Number of obs: 16861, groups:
## ReplicateVial:Line:Population, 1687; Line:Population, 169; Batch, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 68.41419 2.39673 20.26738 28.545 < 2e-16 ***
## PopulationGI 0.05819 2.94895 159.38318 0.020 0.984281
## PopulationKA 4.01793 2.60474 159.45628 1.543 0.124924
## PopulationMA 0.91437 2.60464 159.43197 0.351 0.726012
## PopulationMU 6.73374 2.60442 159.37742 2.586 0.010619 *
## PopulationRE 0.61952 2.77094 159.65533 0.224 0.823372
## PopulationUM 3.52710 2.67959 159.48532 1.316 0.189967
## PopulationVA 6.95578 2.60518 159.56251 2.670 0.008371 **
## PopulationYE -9.98686 2.60612 159.78609 -3.832 0.000182 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.457
## PopulatinKA -0.519 0.420
## PopulatinMA -0.518 0.420 0.476
## PopulatinMU -0.517 0.420 0.476 0.476
## PopulatinRE -0.492 0.395 0.448 0.447 0.447
## PopulatinUM -0.503 0.409 0.463 0.463 0.463 0.435
## PopulatinVA -0.517 0.420 0.476 0.476 0.476 0.447 0.463
## PopulatinYE -0.523 0.420 0.476 0.475 0.476 0.448 0.462 0.475
#Please refer to "_SurvivalAnalyses_" folder
# adding a Censor column, with all individuals being kept
d_SR_surv <- d_SR %>% mutate(Censor = 1)
There are 0 populations and 156 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
For a detailed description of tables, plots, linear models and outputs, please refer here
Abbott Lab : Jessica Abbott, Qinyang Li, Shahzad Khan
Gibert Lab :Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Camille Mermet, Patricia Gibert
Schmidt Lab : Amy Goldfischer, Paul Schmidt
d_Pgm <- read.csv("MasterSheets_Oct21_git/PGM_MasterSheet_Jan22.csv")
str(d_Pgm)
## 'data.frame': 3694 obs. of 21 variables:
## $ Supervisor.PI: chr "Gibert" "Gibert" "Gibert" "Gibert" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 1 ...
## $ AreaT4 : int 12439 10230 7525 14898 9160 8279 13100 9016 7776 9595 ...
## $ AreaT5 : int 10410 8105 7358 7682 8931 8518 16468 10331 7527 8454 ...
## $ AreaT6 : int 8667 6063 6732 5718 8630 10349 11798 7986 5248 8749 ...
## $ PercT4 : num 13.6 28.1 41.4 25.4 17.3 ...
## $ PercT5 : num 27.8 45.8 62.7 37.4 32.8 ...
## $ PercT6 : num 53.9 71 78 44.8 40 ...
## $ TotalArea : int 31516 24398 21615 28298 26721 27146 41366 27333 20551 26798 ...
## $ TotalBlack : num 9270 10900 12986 9220 7966 ...
## $ TotalPerc : num 29.4 44.7 60.1 32.6 29.8 ...
## $ Orientation : chr "OK" "OK" "OK" "OK" ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_Pgm$Supervisor.PI <- as.factor(d_Pgm$Supervisor.PI)
d_Pgm$Diet <- as.factor(d_Pgm$Diet)
d_Pgm$Batch <- as.factor(d_Pgm$Batch)
d_Pgm$Population_Lat <- factor(d_Pgm$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Pgm$Population_Lon <- factor(d_Pgm$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Pgm$Population_Alt <- factor(d_Pgm$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Pgm$Line <- as.factor(d_Pgm$Line)
d_Pgm$AreaT4 <- as.numeric(d_Pgm$AreaT4)
d_Pgm$AreaT5 <- as.numeric(d_Pgm$AreaT5)
d_Pgm$AreaT6 <- as.numeric(d_Pgm$AreaT6)
d_Pgm$PercT4 <- as.numeric(d_Pgm$PercT4)
d_Pgm$PercT5 <- as.numeric(d_Pgm$PercT5)
d_Pgm$PercT6 <- as.numeric(d_Pgm$PercT6)
d_Pgm$TotalArea <- as.numeric(d_Pgm$TotalArea)
d_Pgm$TotalBlack <- as.numeric(d_Pgm$TotalBlack)
d_Pgm$Latitude <- as.numeric(d_Pgm$Latitude)
d_Pgm$Longitude <- as.numeric(d_Pgm$Latitude)
d_Pgm$Altitude <- as.numeric(d_Pgm$Altitude)
str(d_Pgm)
## 'data.frame': 3694 obs. of 24 variables:
## $ Supervisor.PI : Factor w/ 2 levels "Abbott","Gibert": 2 2 2 2 2 2 2 2 2 2 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 168 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 11 ...
## $ Sex : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ Individual : int 1 2 3 4 5 6 7 8 9 1 ...
## $ AreaT4 : num 12439 10230 7525 14898 9160 ...
## $ AreaT5 : num 10410 8105 7358 7682 8931 ...
## $ AreaT6 : num 8667 6063 6732 5718 8630 ...
## $ PercT4 : num 13.6 28.1 41.4 25.4 17.3 ...
## $ PercT5 : num 27.8 45.8 62.7 37.4 32.8 ...
## $ PercT6 : num 53.9 71 78 44.8 40 ...
## $ TotalArea : num 31516 24398 21615 28298 26721 ...
## $ TotalBlack : num 9270 10900 12986 9220 7966 ...
## $ TotalPerc : num 29.4 44.7 60.1 32.6 29.8 ...
## $ Orientation : chr "OK" "OK" "OK" "OK" ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Altitude : num 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat: Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon: Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt: Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_Pgm2 <- read.csv("MasterSheets_Oct21_git/PGM2_MasterSheet_Oct21.csv")
str(d_Pgm2)
## 'data.frame': 1514 obs. of 15 variables:
## $ Supervisor.PI: chr "Schmidt" "Schmidt" "Schmidt" "Schmidt" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Sex : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ Individual : int 2 9 1 10 3 6 8 5 4 7 ...
## $ Tergite8 : int 4 3 2 2 2 2 2 1 1 1 ...
## $ Tergite9 : int 8 7 6 5 4 4 4 3 2 1 ...
## $ Tergite10 : int 6 3 3 4 1 1 1 1 0 0 ...
## $ Total : int 18 13 11 11 7 7 7 5 3 2 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_Pgm2$Supervisor.PI <- as.factor(d_Pgm2$Supervisor.PI)
d_Pgm2$Diet <- as.factor(d_Pgm2$Diet)
d_Pgm2$Batch <- as.factor(d_Pgm2$Batch)
d_Pgm2$Population_Lat <- factor(d_Pgm2$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Pgm2$Population_Lon <- factor(d_Pgm2$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Pgm2$Population_Alt <- factor(d_Pgm2$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Pgm2$Line <- as.factor(d_Pgm2$Line)
d_Pgm2$Tergite8 <- as.numeric(d_Pgm2$Tergite8)
d_Pgm2$Tergite9 <- as.numeric(d_Pgm2$Tergite9)
d_Pgm2$Tergite10 <- as.numeric(d_Pgm2$Tergite10)
d_Pgm2$Total <- as.numeric(d_Pgm2$Total)
str(d_Pgm2)
## 'data.frame': 1514 obs. of 18 variables:
## $ Supervisor.PI : Factor w/ 1 level "Schmidt": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 157 levels "AK1","AK10","AK11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sex : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ Individual : int 2 9 1 10 3 6 8 5 4 7 ...
## $ Tergite8 : num 4 3 2 2 2 2 2 1 1 1 ...
## $ Tergite9 : num 8 7 6 5 4 4 4 3 2 1 ...
## $ Tergite10 : num 6 3 3 4 1 1 1 1 0 0 ...
## $ Total : num 18 13 11 11 7 7 7 5 3 2 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat: Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon: Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt: Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
DT::datatable(table_Pgm_Line_PercT6_wobatch, options = list(pageLength = 10)) %>% formatRound(5:12, 3)
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
DT::datatable(table_Pgm_Line_Tergite9_wbatch, options = list(pageLength = 10)) %>% formatRound(6:13, 3)
Descriptive statistics at the line level, without batch information :
table_Pgm_Line_Tergite9_wobatch <- write.csv(d_Pgm2 %>% group_by(Supervisor.PI, Population, Line) %>% summarise_at(vars(Tergite9), list(Mean = mean, SD= sd, Median = median, Min = min, Max = max, SE = std_err, CV = coef_var, Mode = estimate_mode)), file = "Pigmentation/table_Pgm_Line_Tergite9_wobatch.csv", row.names = T)
table_Pgm_Line_Tergite9_wobatch <- read.csv("Pigmentation/table_Pgm_Line_Tergite9_wobatch.csv")
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
Descriptive statistics at the line level, with batch information :
Descriptive statistics at the line level, without batch information :
Descriptive statistics at the population level, with batch information :
Descriptive statistics at the population level, without batch information :
PercT4
anova(Pgm_lmer_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 3877.1 484.64 8 151.94 15.681 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_PercT4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT4 ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
##
## REML criterion at convergence: 12537.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1997 -0.5926 -0.1274 0.4734 7.9312
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 16.14 4.018
## Residual 30.91 5.559
## Number of obs: 1953, groups: Line:Population, 161
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.2768 0.9638 149.7585 14.813 < 2e-16 ***
## PopulationGI -1.0753 1.5064 151.5438 -0.714 0.47646
## PopulationKA -4.1250 1.3840 151.1631 -2.980 0.00335 **
## PopulationMA 7.3325 1.4042 151.3960 5.222 5.76e-07 ***
## PopulationMU 0.9357 1.4241 150.7292 0.657 0.51216
## PopulationRE 3.0695 1.4497 151.4246 2.117 0.03586 *
## PopulationUM 0.4006 1.4028 150.8162 0.286 0.77560
## PopulationVA -6.7387 1.3818 150.1755 -4.877 2.72e-06 ***
## PopulationYE 0.6671 1.3665 151.3040 0.488 0.62612
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640
## PopulatinKA -0.696 0.446
## PopulatinMA -0.686 0.439 0.478
## PopulatinMU -0.677 0.433 0.471 0.465
## PopulatinRE -0.665 0.425 0.463 0.456 0.450
## PopulatinUM -0.687 0.440 0.478 0.472 0.465 0.457
## PopulatinVA -0.698 0.446 0.486 0.479 0.472 0.464 0.479
## PopulatinYE -0.705 0.451 0.491 0.484 0.477 0.469 0.485 0.492
PercT5
anova(Pgm_lmer_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 3860.7 482.59 8 151.25 13.947 4.48e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_PercT5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT5 ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
##
## REML criterion at convergence: 12736.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1419 -0.6487 -0.1115 0.5154 6.4092
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 15.44 3.929
## Residual 34.60 5.882
## Number of obs: 1953, groups: Line:Population, 161
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.74944 0.95309 148.74070 18.623 < 2e-16 ***
## PopulationGI -1.92457 1.49026 150.78266 -1.291 0.198531
## PopulationKA -5.13874 1.36905 150.35418 -3.754 0.000249 ***
## PopulationMA 5.92107 1.38905 150.61490 4.263 3.55e-05 ***
## PopulationMU -0.44385 1.40858 149.86021 -0.315 0.753123
## PopulationRE 0.66321 1.43411 150.64551 0.462 0.644426
## PopulationUM -0.03034 1.38750 149.95903 -0.022 0.982586
## PopulationVA -7.31175 1.36649 149.22920 -5.351 3.23e-07 ***
## PopulationYE -0.79873 1.35175 150.51384 -0.591 0.555483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640
## PopulatinKA -0.696 0.445
## PopulatinMA -0.686 0.439 0.478
## PopulatinMU -0.677 0.433 0.471 0.464
## PopulatinRE -0.665 0.425 0.463 0.456 0.450
## PopulatinUM -0.687 0.439 0.478 0.471 0.465 0.457
## PopulatinVA -0.697 0.446 0.486 0.479 0.472 0.464 0.479
## PopulatinYE -0.705 0.451 0.491 0.484 0.477 0.469 0.484 0.492
PercT6
anova(Pgm_lmer_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 5124.6 640.58 8 151.7 4.0012 0.0002482 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_PercT6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT6 ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
##
## REML criterion at convergence: 15794.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3191 -0.6368 -0.0337 0.5949 3.1370
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 130.3 11.42
## Residual 160.1 12.65
## Number of obs: 1953, groups: Line:Population, 161
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 34.3826 2.6738 150.2519 12.859 < 2e-16 ***
## PopulationGI -0.4482 4.1750 151.4446 -0.107 0.914645
## PopulationKA -1.5120 3.8365 151.1807 -0.394 0.694052
## PopulationMA 13.1600 3.8919 151.3439 3.381 0.000918 ***
## PopulationMU -2.9065 3.9487 150.8882 -0.736 0.462828
## PopulationRE 8.1644 4.0180 151.3659 2.032 0.043909 *
## PopulationUM 7.4534 3.8893 150.9472 1.916 0.057207 .
## PopulationVA -0.4916 3.8323 150.5155 -0.128 0.898091
## PopulationYE -1.9175 3.7876 151.2768 -0.506 0.613420
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640
## PopulatinKA -0.697 0.446
## PopulatinMA -0.687 0.440 0.479
## PopulatinMU -0.677 0.434 0.472 0.465
## PopulatinRE -0.665 0.426 0.464 0.457 0.451
## PopulatinUM -0.687 0.440 0.479 0.472 0.466 0.457
## PopulatinVA -0.698 0.447 0.486 0.479 0.472 0.464 0.480
## PopulatinYE -0.706 0.452 0.492 0.485 0.478 0.470 0.485 0.493
TotalPerc
anova(Pgm_lmer_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 3253.6 406.7 8 151.49 11.886 4.508e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Abbott_TotalPerc)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TotalPerc ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm, Supervisor.PI == "Abbott"))
##
## REML criterion at convergence: 12772.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7535 -0.6401 -0.0873 0.5628 4.8794
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 23.64 4.862
## Residual 34.22 5.849
## Number of obs: 1953, groups: Line:Population, 161
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 21.0192 1.1475 149.8020 18.317 < 2e-16 ***
## PopulationGI -1.2789 1.7924 151.1867 -0.714 0.47663
## PopulationKA -3.4048 1.6470 150.8841 -2.067 0.04041 *
## PopulationMA 9.0080 1.6708 151.0706 5.391 2.64e-07 ***
## PopulationMU -0.4718 1.6950 150.5454 -0.278 0.78113
## PopulationRE 4.1260 1.7250 151.0950 2.392 0.01799 *
## PopulationUM 2.7754 1.6695 150.6136 1.662 0.09851 .
## PopulationVA -4.8768 1.6449 150.1137 -2.965 0.00352 **
## PopulationYE -0.2038 1.6260 150.9949 -0.125 0.90043
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640
## PopulatinKA -0.697 0.446
## PopulatinMA -0.687 0.440 0.479
## PopulatinMU -0.677 0.433 0.472 0.465
## PopulatinRE -0.665 0.426 0.464 0.457 0.450
## PopulatinUM -0.687 0.440 0.479 0.472 0.465 0.457
## PopulatinVA -0.698 0.447 0.486 0.479 0.472 0.464 0.480
## PopulatinYE -0.706 0.452 0.492 0.485 0.478 0.469 0.485 0.492
PercT4
anova(Pgm_lmer_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1861.6 232.7 8 151.19 2.5618 0.01198 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Gibert_PercT4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT4 ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
##
## REML criterion at convergence: 12935.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4920 -0.6719 -0.1283 0.5731 5.9803
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 15.601 3.950
## Batch (Intercept) 3.226 1.796
## Residual 90.836 9.531
## Number of obs: 1741, groups: Line:Population, 167; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 24.28251 1.57941 4.55352 15.374 4.25e-05 ***
## PopulationGI -0.88788 1.69837 151.69405 -0.523 0.6019
## PopulationKA -3.42364 1.57466 152.48388 -2.174 0.0312 *
## PopulationMA -0.50222 1.59924 153.61400 -0.314 0.7539
## PopulationMU 0.78961 1.57540 152.75242 0.501 0.6169
## PopulationRE -4.31393 1.66776 151.86314 -2.587 0.0106 *
## PopulationUM 0.19090 1.61666 152.21307 0.118 0.9062
## PopulationVA 0.02432 1.59518 152.39674 0.015 0.9879
## PopulationYE 1.11868 1.58777 156.43329 0.705 0.4821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.425
## PopulatinKA -0.459 0.469
## PopulatinMA -0.452 0.462 0.500
## PopulatinMU -0.458 0.469 0.508 0.500
## PopulatinRE -0.432 0.445 0.478 0.471 0.478
## PopulatinUM -0.447 0.457 0.495 0.487 0.494 0.466
## PopulatinVA -0.453 0.463 0.501 0.494 0.501 0.472 0.488
## PopulatinYE -0.455 0.466 0.504 0.496 0.503 0.474 0.491 0.497
PercT5
anova(Pgm_lmer_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1803.1 225.39 8 157.32 2.1354 0.03548 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm_lmer_Gibert_PercT5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT5 ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
##
## REML criterion at convergence: 13270.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9328 -0.6447 -0.0768 0.5422 5.7227
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 36.17 6.014
## Residual 105.55 10.274
## Number of obs: 1741, groups: Line:Population, 167
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.60294 1.52456 156.95329 21.385 <2e-16 ***
## PopulationGI -3.45120 2.32659 156.32514 -1.483 0.1400
## PopulationKA -3.27749 2.15540 156.76946 -1.521 0.1304
## PopulationMA 3.79005 2.18722 157.65924 1.733 0.0851 .
## PopulationMU 1.93616 2.15605 156.95329 0.898 0.3706
## PopulationRE -1.70093 2.28506 156.44381 -0.744 0.4578
## PopulationUM 0.08672 2.21401 156.63100 0.039 0.9688
## PopulationVA -0.54513 2.18337 156.70526 -0.250 0.8032
## PopulationYE 0.60097 2.16758 159.78466 0.277 0.7819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655
## PopulatinKA -0.707 0.463
## PopulatinMA -0.697 0.457 0.493
## PopulatinMU -0.707 0.463 0.500 0.493
## PopulatinRE -0.667 0.437 0.472 0.465 0.472
## PopulatinUM -0.689 0.451 0.487 0.480 0.487 0.459
## PopulatinVA -0.698 0.458 0.494 0.487 0.494 0.466 0.481
## PopulatinYE -0.703 0.461 0.497 0.490 0.497 0.469 0.484 0.491
PercT6
anova(Pgm_lmer_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 2429.4 303.67 8 158.47 1.4648 0.1741
summary(Pgm_lmer_Gibert_PercT6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PercT6 ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
##
## REML criterion at convergence: 14560.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0930 -0.6255 0.0378 0.6407 2.8805
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 173.498 13.172
## Batch (Intercept) 9.633 3.104
## Residual 207.318 14.399
## Number of obs: 1741, groups: Line:Population, 167; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.7258 3.6874 10.3399 12.672 1.25e-07 ***
## PopulationGI -0.5588 4.7688 158.8324 -0.117 0.9069
## PopulationKA 11.3249 4.4166 158.9499 2.564 0.0113 *
## PopulationMA 8.3252 4.4785 159.4865 1.859 0.0649 .
## PopulationMU 5.6397 4.4172 159.0392 1.277 0.2035
## PopulationRE 7.5091 4.6823 158.7993 1.604 0.1108
## PopulationUM 6.9007 4.5365 158.8765 1.521 0.1302
## PopulationVA 4.6139 4.4746 158.9709 1.031 0.3040
## PopulationYE 2.3744 4.4294 160.6488 0.536 0.5927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.534
## PopulatinKA -0.577 0.466
## PopulatinMA -0.569 0.459 0.497
## PopulatinMU -0.577 0.465 0.503 0.497
## PopulatinRE -0.544 0.440 0.474 0.468 0.474
## PopulatinUM -0.562 0.453 0.490 0.484 0.490 0.462
## PopulatinVA -0.570 0.459 0.497 0.490 0.497 0.468 0.484
## PopulatinYE -0.575 0.464 0.502 0.495 0.502 0.473 0.489 0.496
TotalPerc
## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 497.78 62.223 8 157.94 0.8462 0.5634
summary(Pgm_lmer_Gibert_TotalPerc)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TotalPerc ~ Population + (1 | Line:Population) + (1 | Batch)
## Data: (subset(d_Pgm, Supervisor.PI == "Gibert"))
##
## REML criterion at convergence: 12682.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2632 -0.6733 -0.0496 0.6084 4.5241
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 34.06 5.836
## Batch (Intercept) 0.00 0.000
## Residual 73.53 8.575
## Number of obs: 1741, groups: Line:Population, 167; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 33.30713 1.43614 157.60162 23.192 <2e-16 ***
## PopulationGI -1.88256 2.19213 157.11869 -0.859 0.392
## PopulationKA 0.13794 2.03052 157.45431 0.068 0.946
## PopulationMA 2.82784 2.05986 158.20548 1.373 0.172
## PopulationMU 2.07261 2.03101 157.60162 1.020 0.309
## PopulationRE 0.04646 2.15291 157.20835 0.022 0.983
## PopulationUM 1.99631 2.08584 157.34973 0.957 0.340
## PopulationVA 1.23563 2.05692 157.40581 0.601 0.549
## PopulationYE 1.19800 2.03985 159.99603 0.587 0.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655
## PopulatinKA -0.707 0.463
## PopulatinMA -0.697 0.457 0.493
## PopulatinMU -0.707 0.463 0.500 0.493
## PopulatinRE -0.667 0.437 0.472 0.465 0.472
## PopulatinUM -0.689 0.451 0.487 0.480 0.487 0.459
## PopulatinVA -0.698 0.457 0.494 0.487 0.494 0.466 0.481
## PopulatinYE -0.704 0.461 0.498 0.491 0.498 0.470 0.485 0.492
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
Tergite8
anova(Pgm2_lmer_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 22.509 2.8137 8 147.42 3.9144 0.0003221 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm2_lmer_Schmidt_Tergite8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Tergite8 ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
##
## REML criterion at convergence: 4128.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3233 -0.6262 -0.0680 0.5288 7.2893
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.5292 0.7274
## Residual 0.7188 0.8478
## Number of obs: 1514, groups: Line:Population, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.27000 0.17336 145.49306 13.094 <2e-16 ***
## PopulationGI -0.26045 0.27736 147.80273 -0.939 0.3492
## PopulationKA 0.22474 0.24837 145.49306 0.905 0.3670
## PopulationMA 0.58606 0.24893 146.62982 2.354 0.0199 *
## PopulationMU 0.01145 0.24532 145.83263 0.047 0.9628
## PopulationRE 0.20939 0.27165 148.53344 0.771 0.4421
## PopulationUM 0.53207 0.26518 146.26029 2.006 0.0467 *
## PopulationVA -0.62944 0.25587 145.75515 -2.460 0.0151 *
## PopulationYE 0.16110 0.24595 147.30477 0.655 0.5135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.625
## PopulatinKA -0.698 0.436
## PopulatinMA -0.696 0.435 0.486
## PopulatinMU -0.707 0.442 0.493 0.492
## PopulatinRE -0.638 0.399 0.445 0.444 0.451
## PopulatinUM -0.654 0.409 0.456 0.455 0.462 0.417
## PopulatinVA -0.678 0.423 0.473 0.472 0.479 0.432 0.443
## PopulatinYE -0.705 0.441 0.492 0.491 0.498 0.450 0.461 0.478
Tergite9
anova(Pgm2_lmer_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 31.677 3.9597 8 148.1 1.5538 0.1436
summary(Pgm2_lmer_Schmidt_Tergite9)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Tergite9 ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
##
## REML criterion at convergence: 6123.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2209 -0.6482 -0.0807 0.5799 3.6192
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 3.671 1.916
## Residual 2.548 1.596
## Number of obs: 1514, groups: Line:Population, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.5450 0.4430 146.9794 10.258 <2e-16 ***
## PopulationGI -0.4001 0.7075 148.3076 -0.566 0.5726
## PopulationKA 0.4866 0.6348 146.9794 0.767 0.4446
## PopulationMA 0.9622 0.6356 147.6853 1.514 0.1322
## PopulationMU 0.3373 0.6268 147.1736 0.538 0.5913
## PopulationRE 0.9606 0.6926 148.7380 1.387 0.1675
## PopulationUM 1.7584 0.6773 147.4275 2.596 0.0104 *
## PopulationVA 0.3181 0.6538 147.1266 0.487 0.6273
## PopulationYE 0.2175 0.6277 148.0006 0.347 0.7294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.626
## PopulatinKA -0.698 0.437
## PopulatinMA -0.697 0.437 0.487
## PopulatinMU -0.707 0.443 0.493 0.493
## PopulatinRE -0.640 0.401 0.447 0.446 0.452
## PopulatinUM -0.654 0.410 0.457 0.456 0.462 0.418
## PopulatinVA -0.678 0.424 0.473 0.472 0.479 0.434 0.443
## PopulatinYE -0.706 0.442 0.493 0.492 0.499 0.452 0.462 0.478
Tergite10
anova(Pgm2_lmer_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 78.149 9.7686 8 148.36 2.3141 0.02283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm2_lmer_Schmidt_Tergite10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Tergite10 ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
##
## REML criterion at convergence: 6832.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7236 -0.6439 -0.1671 0.6539 3.2399
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 4.184 2.045
## Residual 4.221 2.055
## Number of obs: 1514, groups: Line:Population, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.1450 0.4799 146.8229 4.470 1.56e-05 ***
## PopulationGI 0.1159 0.7671 148.6520 0.151 0.880056
## PopulationKA 1.4287 0.6875 146.8229 2.078 0.039454 *
## PopulationMA 1.6307 0.6888 147.7582 2.368 0.019197 *
## PopulationMU 0.8444 0.6790 147.0910 1.244 0.215633
## PopulationRE 0.9257 0.7511 149.2376 1.232 0.219716
## PopulationUM 2.6361 0.7339 147.4353 3.592 0.000446 ***
## PopulationVA 1.6089 0.7082 147.0280 2.272 0.024555 *
## PopulationYE 1.2194 0.6803 148.2432 1.792 0.075122 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.626
## PopulatinKA -0.698 0.437
## PopulatinMA -0.697 0.436 0.486
## PopulatinMU -0.707 0.442 0.493 0.492
## PopulatinRE -0.639 0.400 0.446 0.445 0.452
## PopulatinUM -0.654 0.409 0.456 0.456 0.462 0.418
## PopulatinVA -0.678 0.424 0.473 0.472 0.479 0.433 0.443
## PopulatinYE -0.705 0.441 0.492 0.491 0.499 0.451 0.461 0.478
Total
anova(Pgm2_lmer_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 232.51 29.064 8 148.08 2.1415 0.03533 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Pgm2_lmer_Schmidt_Total)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Total ~ Population + (1 | Line:Population)
## Data: (subset(d_Pgm2, Supervisor.PI == "Schmidt"))
##
## REML criterion at convergence: 8630.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7804 -0.6403 -0.1225 0.6108 4.0733
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 18.12 4.257
## Residual 13.57 3.684
## Number of obs: 1514, groups: Line:Population, 157
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.9600 0.9869 146.8837 9.079 6.73e-16 ***
## PopulationGI -0.5480 1.5763 148.3022 -0.348 0.72860
## PopulationKA 2.1400 1.4140 146.8837 1.513 0.13231
## PopulationMA 3.1858 1.4159 147.6323 2.250 0.02593 *
## PopulationMU 1.1931 1.3962 147.0911 0.855 0.39420
## PopulationRE 2.0855 1.5431 148.7609 1.351 0.17859
## PopulationUM 4.9263 1.5088 147.3616 3.265 0.00136 **
## PopulationVA 1.2988 1.4564 147.0412 0.892 0.37398
## PopulationYE 1.5982 1.3984 147.9763 1.143 0.25492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.626
## PopulatinKA -0.698 0.437
## PopulatinMA -0.697 0.436 0.487
## PopulatinMU -0.707 0.443 0.493 0.493
## PopulatinRE -0.640 0.400 0.446 0.446 0.452
## PopulatinUM -0.654 0.410 0.457 0.456 0.462 0.418
## PopulatinVA -0.678 0.424 0.473 0.472 0.479 0.433 0.443
## PopulatinYE -0.706 0.442 0.493 0.492 0.499 0.451 0.462 0.478
There are 9 populations and 149 isofemale lines that have been phenotyped across the labs. Please refer to tables named intersect_Trait_level (i.e. intersect_Via_pop) for details.
Females
Males
Females
Males
Females
Males
anova(Via_lmer_Lat_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.068572 0.068572 1 7.1226 5.521 0.0505 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Via_lmer_Lat_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.047793 0.047793 1 1.0297 2.3848 0.3607
anova(Via_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.14003 0.14003 1 7.3684 13.595 0.007111 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Via_lmer_Lat_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.14657 0.14657 1 6.7226 1.793 0.2241
anova(Via_lmer_Lat_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.019734 0.019734 1 7.1768 1.6379 0.2404
anova(Via_lmer_Lat_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.32986 0.32986 1 7.0542 9.9389 0.01593 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dir.create(file.path("DevelopmentTime"), showWarnings = FALSE)
anova(DT_P_lmer_Lat)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 148.08 148.08 1 6.9929 1.2438 0.3016
anova(DT_A_F_lmer_Lat_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 4.9192 4.9192 1 6.8757 0.0506 0.8285
anova(DT_A_M_lmer_Lat_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 2.426 2.426 1 6.7608 0.0222 0.8859
anova(DT_A_F_lmer_Lat_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 152.75 152.75 1 1.0154 0.486 0.611
anova(DT_A_M_lmer_Lat_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 40.186 40.186 1 1.0316 0.1335 0.7755
anova(DT_A_F_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1046.5 1046.5 1 6.8151 6.746 0.03641 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1193.7 1193.7 1 6.8075 5.7584 0.04847 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_F_lmer_Lat_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 12.366 12.366 1 7.3289 0.057 0.8178
anova(DT_A_M_lmer_Lat_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.04256 0.04256 1 6.9309 1e-04 0.9908
anova(DT_A_F_lmer_Lat_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 353.65 353.65 1 7.1388 0.8847 0.3776
anova(DT_A_M_lmer_Lat_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 309.45 309.45 1 7.1523 0.7254 0.422
anova(DT_A_F_lmer_Lat_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 101.86 101.86 1 6.9315 0.8691 0.3825
anova(DT_A_M_lmer_Lat_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 22.846 22.846 1 6.6572 0.1783 0.6862
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0024786 (tol = 0.002, component 1)
anova(DW_F_lmer_Lat_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.0010313 0.0010313 1 6.8158 0.3827 0.5563
anova(DW_M_lmer_Lat_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.00022165 0.00022165 1 7.028 0.3095 0.5952
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00318688 (tol = 0.002, component 1)
anova(DW_F_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.00018731 0.00018731 1 7.1001 0.0829 0.7816
## boundary (singular) fit: see ?isSingular
anova(DW_M_lmer_Lat_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.00035052 0.00035052 1 164.94 0.5114 0.4755
anova(DW_F_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 5.1001e-05 5.1001e-05 1 7.0236 0.0192 0.8937
anova(DW_M_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.0001985 0.0001985 1 6.873 0.2161 0.6564
anova(TL_F_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 2090.9 2090.9 1 6.6825 0.8191 0.3969
anova(TL_M_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 922.58 922.58 1 6.7735 0.41 0.543
anova(TL_F_lmer_Lat_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 2549.8 2549.8 1 7 1.6586 0.2387
anova(TL_M_lmer_Lat_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1361.8 1361.8 1 7 1.1207 0.3249
## boundary (singular) fit: see ?isSingular
anova(TL_F_lmer_Lat_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 6315.9 6315.9 1 23.94 2.2425 0.1473
## boundary (singular) fit: see ?isSingular
anova(TL_M_lmer_Lat_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 2407.5 2407.5 1 24.045 0.6726 0.4202
anova(TL_F_lmer_Lat_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 7587.3 7587.3 1 6.6722 6.0178 0.04557 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(WA_Lat_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 7896.8 7896.8 1 6.9295 1.2238 0.3055
anova(WA_Lat_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 3135.5 3135.5 1 6.9311 0.5964 0.4655
## boundary (singular) fit: see ?isSingular
anova(WA_Lat_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 54803 54803 1 25 6.5631 0.01682 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Lat_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 11392 11392 1 25 1.9002 0.1803
anova(WA_Lat_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 174.22 174.22 1 7.826 0.0134 0.9106
anova(WA_Lat_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 24.429 24.429 1 7.7198 0.0025 0.9615
anova(WA_Lat_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 26336 26336 1 6.8837 3.402 0.1084
anova(WA_Lat_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 22919 22919 1 6.9146 3.8312 0.09169 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(WA_Lat_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 7711.5 7711.5 1 6.9316 1.1933 0.3112
anova(WA_Lat_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 3176.2 3176.2 1 6.9296 0.5972 0.4652
## boundary (singular) fit: see ?isSingular
anova(WA_Lat_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 57489 57489 1 25 6.6128 0.01646 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Lat_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 11736 11736 1 25 1.9456 0.1753
anova(WA_Lat_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 153.16 153.16 1 7.7755 0.0117 0.9167
anova(WA_Lat_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.040336 0.040336 1 7.6741 0 0.9984
anova(WA_Lat_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 27172 27172 1 6.8816 3.5256 0.1032
anova(WA_Lat_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 21431 21431 1 6.908 3.6506 0.09822 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dir.create(file.path("Fecundity"), showWarnings = FALSE)
anova(Fec_lmer_Lat_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 18138 18138 1 6.8508 6.565 0.03812 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(Fec_lmer_Lat_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 215.56 215.56 1 162.2 0.1708 0.6799
dir.create(file.path("Lifespan"), showWarnings = FALSE)
anova(LS_Lat_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 2894.9 2894.9 1 7.0035 17.915 0.003871 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(LS_Lat_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 140.55 140.55 1 7.004 0.7886 0.404
anova(LS_Lat_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1161.9 1161.9 1 6.9763 3.7271 0.09498 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(LS_Lat_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 6667.5 6667.5 1 7.0283 18.136 0.003719 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(LS_Lat_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 289.98 289.98 1 6.7697 1.1771 0.3151
anova(LS_Lat_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1160.8 1160.8 1 6.9458 3.5886 0.1004
anova(CSM_F_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.066037 0.066037 1 6.6147 1.4592 0.2685
anova(CSM_M_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.091607 0.091607 1 6.2138 1.0344 0.3471
## boundary (singular) fit: see ?isSingular
anova(CSM_F_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.037639 0.037639 1 121.44 0.2851 0.5944
## boundary (singular) fit: see ?isSingular
anova(CSM_M_lmer_Lat_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.049652 0.049652 1 120.96 0.3545 0.5527
anova(CSM_F_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.039207 0.039207 1 7.3591 1.3043 0.2892
anova(CSM_M_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.037822 0.037822 1 7.1265 1.0164 0.3464
dir.create(file.path("ChillComa"), showWarnings = FALSE)
anova(CCRT_F_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 61.122 61.122 1 7.122 3e-04 0.9859
anova(CCRT_M_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 290488 290488 1 6.8661 1.8478 0.217
For a detailed description of tables, plots, linear models and outputs, please refer here
anova(HSM_F_lmer_Lat_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 35232 35232 1 7.0013 5.3708 0.05358 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(HSM_M_lmer_Lat_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 52510 52510 1 7.0012 9.8787 0.01631 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(HSM_F_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 156.27 156.27 1 6.96 0.0513 0.8274
anova(HSM_M_lmer_Lat_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 4723.9 4723.9 1 6.5508 1.5857 0.251
anova(Dia_lmer_Lat_Bergland)
anova(Dia_lmer_Lat_Flatt)
anova(Dia_lmer_Lat_Schlotterer)
anova(LA_lmer_Lat_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.037349 0.037349 1 7.9016 0.2526 0.629
CircPhase
anova(LA_lmer_Lat_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 61.725 61.725 1 7.2616 1.3242 0.2863
Activity
anova(LA_lmer_Lat_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 85.098 85.098 1 6.779 1.1785 0.3148
anova(SR_F_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 2767.2 2767.2 1 7.2849 6.4834 0.03707 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_M_lmer_Lat_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1281.4 1281.4 1 7.2192 5.8942 0.04453 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_F_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1576.1 1576.1 1 7.2393 4.8876 0.06147 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_M_lmer_Lat_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 764.76 764.76 1 7.2317 3.8985 0.08759 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_F_lmer_Lat_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 874.85 874.85 1 6.8839 1.82 0.22
anova(SR_M_lmer_Lat_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 707.52 707.52 1 7.1623 2.5014 0.1568
PercT4
anova(Pgm_lmer_Lat_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 63.659 63.659 1 6.988 2.0598 0.1944
PercT5
anova(Pgm_lmer_Lat_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 40.645 40.645 1 7.0027 1.1746 0.3144
PercT6
anova(Pgm_lmer_Lat_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 50.979 50.979 1 6.8993 0.3184 0.5904
TotalPerc
anova(Pgm_lmer_Lat_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 39.59 39.59 1 6.969 1.1571 0.3179
PercT4
anova(Pgm_lmer_Lat_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 6.3924 6.3924 1 6.8915 0.0706 0.7983
PercT5
anova(Pgm_lmer_Lat_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.043179 0.043179 1 6.9001 4e-04 0.9844
PercT6
anova(Pgm_lmer_Lat_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 3.6662 3.6662 1 7.0273 0.0176 0.898
TotalPerc
## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Lat_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.50455 0.50455 1 165.34 0.0069 0.9341
Tergite8
anova(Pgm2_lmer_Lat_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.38967 0.38967 1 6.7828 0.5421 0.4862
Tergite9
anova(Pgm2_lmer_Lat_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.19172 0.19172 1 5.8379 0.0752 0.7933
Tergite10
anova(Pgm2_lmer_Lat_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 0.019915 0.019915 1 6.5722 0.0047 0.9473
Total
anova(Pgm2_lmer_Lat_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Latitude 1.2739 1.2739 1 6.2808 0.0939 0.7692
anova(Via_lmer_Long_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0010065 0.0010065 1 7.1692 0.081 0.7839
anova(Via_lmer_Long_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.013299 0.013299 1 1.0042 0.6636 0.5644
anova(Via_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.00087846 0.00087846 1 7.2565 0.0853 0.7784
anova(Via_lmer_Long_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.00078423 0.00078423 1 7.2723 0.0096 0.9246
anova(Via_lmer_Long_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.017496 0.017496 1 7.4444 1.4521 0.2651
anova(Via_lmer_Long_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0042991 0.0042991 1 7.178 0.1296 0.7292
dir.create(file.path("DevelopmentTime"), showWarnings = FALSE)
anova(DT_P_lmer_Long)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.057398 0.057398 1 7.3594 5e-04 0.9831
anova(DT_A_F_lmer_Long_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 442.95 442.95 1 7.5199 4.5593 0.06743 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Long_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 142.01 142.01 1 7.1837 1.3004 0.2907
DT_A_F_lmer_Long_Grath <- lmer(DT_EggAdult ~ Longitude + (1|Population) + (1|Line:Population), data = d_DT_A_F[d_DT_A_F$Supervisor.PI == "Grath",])
capture.output(summary(DT_A_F_lmer_Long_Grath),file = "DevelopmentTime/DT_A_F_lmer_Long_Grath_sum.txt")
capture.output(anova(DT_A_F_lmer_Long_Grath),file = "DevelopmentTime/DT_A_F_lmer_Long_Grath.txt")
anova(DT_A_F_lmer_Long_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 520.6 520.6 1 1.1943 1.6566 0.3932
anova(DT_A_M_lmer_Long_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 174.67 174.67 1 1.3194 0.5804 0.5587
anova(DT_A_F_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 823.09 823.09 1 7.3767 5.3056 0.05287 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
DT_A_M_lmer_Long_Hoedjes <- lmer(DT_EggAdult ~ Longitude + (1|Population) + (1|Line:Population) ,data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Hoedjes",])
capture.output(summary(DT_A_M_lmer_Long_Hoedjes),file = "DevelopmentTime/DT_A_M_lmer_Long_Hoedjes_sum.txt")
capture.output(anova(DT_A_M_lmer_Long_Hoedjes),file = "DevelopmentTime/DT_A_M_lmer_Long_Hoedjes.txt")
anova(DT_A_M_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 1180.2 1180.2 1 7.4073 5.6936 0.04653 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_F_lmer_Long_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 29.616 29.616 1 7.7873 0.1366 0.7215
DT_A_M_lmer_Long_Schmidt <- lmer(DT_EggAdult ~ Longitude + (1|Population) + (1|Line:Population), data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Schmidt",])
capture.output(summary(DT_A_M_lmer_Long_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Long_Schmidt_sum.txt")
capture.output(anova(DT_A_M_lmer_Long_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Long_Schmidt.txt")
anova(DT_A_M_lmer_Long_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 17.638 17.638 1 7.3191 0.0594 0.8142
anova(DT_A_F_lmer_Long_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 1.3037 1.3037 1 7.2564 0.0033 0.956
anova(DT_A_M_lmer_Long_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 5.495 5.495 1 7.2605 0.0129 0.9127
anova(DT_A_F_lmer_Long_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 410.18 410.18 1 8.1104 3.4997 0.09779 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Long_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 1001.8 1001.8 1 8.2231 7.8178 0.02272 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DW_F_lmer_Long_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0025578 0.0025578 1 7.3126 0.9491 0.3611
anova(DW_M_lmer_Long_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.00087809 0.00087809 1 7.2443 1.2263 0.3035
anova(DW_F_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.00068713 0.00068713 1 7.8502 0.3041 0.5967
## boundary (singular) fit: see ?isSingular
anova(DW_M_lmer_Long_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.00041152 0.00041152 1 164.84 0.6004 0.4395
anova(DW_F_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.00013742 0.00013742 1 7.207 0.0517 0.8265
anova(DW_M_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 1.7215e-05 1.7215e-05 1 7.2064 0.0187 0.8949
dir.create(file.path("ThoraxLength"), showWarnings = FALSE)
anova(TL_F_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 51.134 51.134 1 7.2101 0.02 0.8913
anova(TL_M_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 110.19 110.19 1 7.2587 0.049 0.831
anova(TL_F_lmer_Long_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 26.558 26.558 1 6.9994 0.0173 0.8991
anova(TL_M_lmer_Long_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 55.595 55.595 1 7.0004 0.0458 0.8367
## boundary (singular) fit: see ?isSingular
anova(TL_F_lmer_Long_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 1603.5 1603.5 1 23.897 0.5693 0.4579
TL_M_lmer_Long_Ritchie <- lmer(TL_micrometers ~ Longitude + (1|Population) + (1|Line:Population), data = (subset(d_TL_M,Supervisor.PI=='Ritchie')))
## boundary (singular) fit: see ?isSingular
capture.output(summary(TL_M_lmer_Long_Ritchie),file = "ThoraxLength/TL_M_Ritchie_sum.txt")
capture.output(anova(TL_M_lmer_Long_Ritchie),file = "ThoraxLength/TL_M_lmer_Long_Ritchie.txt")
anova(TL_M_lmer_Long_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 3131 3131 1 24.071 0.8747 0.3589
TL_F_lmer_Long_Schmidt <- lmer(TL_micrometers ~ Longitude + (1|Population) + (1|Line:Population), data = (subset(d_TL_F,Supervisor.PI=='Schmidt')))
capture.output(summary(TL_F_lmer_Long_Schmidt),file = "ThoraxLength/TL_F_Schmidt_sum.txt")
capture.output(anova(TL_F_lmer_Long_Schmidt),file = "ThoraxLength/TL_F_lmer_Long_Schmidt.txt")
anova(TL_F_lmer_Long_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 5017.8 5017.8 1 7.7441 3.9791 0.08235 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(WA_Long_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 21030 21030 1 7.0413 3.2589 0.1138
anova(WA_Long_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 11117 11117 1 7.0742 2.1144 0.1888
anova(WA_Long_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 31435 31435 1 7.0004 3.7646 0.0935 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Long_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 4259.1 4259.1 1 25 0.7104 0.4073
anova(WA_Long_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 5945.6 5945.6 1 7.3399 0.4589 0.5189
anova(WA_Long_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 57.349 57.349 1 7.0233 0.0058 0.9412
anova(WA_Long_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 13008 13008 1 7.1043 1.6804 0.2354
anova(WA_Long_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 9380.6 9380.6 1 7.1449 1.5681 0.2499
anova(WA_Long_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 21347 21347 1 7.0421 3.3033 0.1117
anova(WA_Long_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 12027 12027 1 7.0764 2.2614 0.1759
anova(WA_Long_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 31861 31861 1 7.0001 3.6649 0.09713 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(WA_Long_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 4851.5 4851.5 1 24.999 0.8043 0.3784
anova(WA_Long_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 5768.1 5768.1 1 7.2969 0.4393 0.5278
anova(WA_Long_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 6.5633 6.5633 1 7.0183 7e-04 0.98
anova(WA_Long_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 12085 12085 1 7.0993 1.5681 0.2502
anova(WA_Long_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 8262.2 8262.2 1 7.1372 1.4074 0.2735
dir.create(file.path("Fecundity"), showWarnings = FALSE)
anova(Fec_lmer_Long_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 594.33 594.33 1 7.519 0.2151 0.656
## boundary (singular) fit: see ?isSingular
anova(Fec_lmer_Long_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 168.63 168.63 1 162.13 0.1336 0.7152
anova(LS_Long_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 419.22 419.22 1 6.9999 2.5943 0.1513
anova(LS_Long_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 12.113 12.113 1 6.9904 0.068 0.8018
anova(LS_Long_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 332.72 332.72 1 6.9913 1.0673 0.336
anova(LS_Long_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 1095.2 1095.2 1 6.9951 2.9789 0.128
anova(LS_Long_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 623.22 623.22 1 7.6402 2.5297 0.1522
anova(LS_Long_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 341.74 341.74 1 7.3639 1.0565 0.3366
dir.create(file.path("ColdShock"), showWarnings = FALSE)
anova(CSM_F_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0079988 0.0079988 1 7.1065 0.1768 0.6865
anova(CSM_M_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0001445 0.0001445 1 7.5298 0.0016 0.9688
## boundary (singular) fit: see ?isSingular
anova(CSM_F_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.016849 0.016849 1 114.22 0.1274 0.7218
## boundary (singular) fit: see ?isSingular
anova(CSM_M_lmer_Long_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0042714 0.0042714 1 113.22 0.0305 0.8617
anova(CSM_F_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0064623 0.0064623 1 7.8424 0.215 0.6555
anova(CSM_M_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.0079713 0.0079713 1 7.6665 0.2142 0.6563
anova(CCRT_F_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 90246 90246 1 7.291 0.4922 0.5047
anova(CCRT_M_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 41945 41945 1 7.0115 0.2668 0.6214
For a detailed description of tables, plots, linear models and outputs, please refer here
anova(HSM_F_lmer_Long_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 2583.4 2583.4 1 7.0335 0.3938 0.5501
anova(HSM_M_lmer_Long_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 4076.2 4076.2 1 7.0029 0.7668 0.4102
anova(HSM_F_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 137.31 137.31 1 7.0495 0.0451 0.8379
anova(HSM_M_lmer_Long_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 1496.3 1496.3 1 6.7623 0.5023 0.5022
anova(Dia_lmer_Long_Bergland)
anova(Dia_lmer_Long_Flatt)
anova(Dia_lmer_Long_Schlotterer)
dir.create(file.path("CircadianEclosion"), showWarnings = FALSE)
dir.create(file.path("Locomotor"), showWarnings = FALSE)
ND
anova(LA_lmer_Long_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.056798 0.056798 1 8.8887 0.3841 0.551
CircPhase
LA_lmer_Long_CircPhase_Tauber <- lmer(CircPhase ~ Longitude + (1|Population) + (1|Line:Population),data = (d_LA))
capture.output(summary(LA_lmer_Long_CircPhase_Tauber),file = "Locomotor/LA_lmer_Long_CircPhase_Tauber_sum.txt")
capture.output(anova(LA_lmer_Long_CircPhase_Tauber),file = "Locomotor/LA_lmer_Long_CircPhase_Tauber.txt")
anova(LA_lmer_Long_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 49.877 49.877 1 7.7857 1.0684 0.3323
Activity
anova(LA_lmer_Long_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 189.68 189.68 1 7.3222 2.6277 0.1472
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00313532 (tol = 0.002, component 1)
anova(SR_F_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 43.103 43.103 1 7.5547 0.101 0.7593
anova(SR_M_lmer_Long_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.53909 0.53909 1 7.2918 0.0025 0.9616
anova(SR_F_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 10.72 10.72 1 7.2773 0.0332 0.8603
anova(SR_M_lmer_Long_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 18.713 18.713 1 7.372 0.0954 0.766
anova(SR_F_lmer_Long_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 285.73 285.73 1 7.3486 0.5944 0.4648
anova(SR_M_lmer_Long_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 29.939 29.939 1 7.2776 0.1058 0.7541
PercT4
anova(Pgm_lmer_Long_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 63.659 63.659 1 6.988 2.0598 0.1944
PercT5
anova(Pgm_lmer_Long_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 40.645 40.645 1 7.0027 1.1746 0.3144
PercT6
Pgm_lmer_Long_Abbott_PercT6 <- lmer(PercT6 ~ Longitude + (1|Population) + (1|Line:Population),data = (subset(d_Pgm,Supervisor.PI=='Abbott')))
capture.output(summary(Pgm_lmer_Long_Abbott_PercT6),file = "Pigmentation/Pgm_lmer_Long_Abbott_PercT6_sum.txt")
capture.output(anova(Pgm_lmer_Long_Abbott_PercT6),file = "Pigmentation/Pgm_lmer_Long_Abbott_PercT6.txt")
anova(Pgm_lmer_Long_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 50.979 50.979 1 6.8993 0.3184 0.5904
TotalPerc
anova(Pgm_lmer_Long_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 39.59 39.59 1 6.969 1.1571 0.3179
PercT4
anova(Pgm_lmer_Long_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 6.3924 6.3924 1 6.8915 0.0706 0.7983
PercT5
anova(Pgm_lmer_Long_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.043179 0.043179 1 6.9001 4e-04 0.9844
PercT6
anova(Pgm_lmer_Long_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 3.6662 3.6662 1 7.0273 0.0176 0.898
TotalPerc
## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Long_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.50455 0.50455 1 165.34 0.0069 0.9341
Tergite8
anova(Pgm2_lmer_Long_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.050033 0.050033 1 7.2265 0.0696 0.7993
Tergite9
anova(Pgm2_lmer_Long_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 0.17477 0.17477 1 6.9364 0.0686 0.801
Tergite10
anova(Pgm2_lmer_Long_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 6.2458 6.2458 1 7.7863 1.4796 0.2594
Total
anova(Pgm2_lmer_Long_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Longitude 5.2988 5.2988 1 7.2798 0.3904 0.5511
dir.create(file.path("Viability"), showWarnings = FALSE)
anova(Via_lmer_Alt_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.041186 0.041186 1 6.8919 3.316 0.1121
anova(Via_lmer_Alt_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.035661 0.035661 1 0.99521 1.7797 0.4103
anova(Via_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.0092781 0.0092781 1 6.8246 0.9008 0.375
anova(Via_lmer_Alt_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.11234 0.11234 1 6.2853 1.3732 0.2838
anova(Via_lmer_Alt_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.027819 0.027819 1 7.0405 2.309 0.1722
anova(Via_lmer_Alt_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.1106 0.1106 1 6.7736 3.3331 0.1121
dir.create(file.path("DevelopmentTime"), showWarnings = FALSE)
anova(DT_P_lmer_Alt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 12.886 12.886 1 6.4564 0.1082 0.7526
anova(DT_A_F_lmer_Alt_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 394.07 394.07 1 6.6617 4.056 0.08594 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DT_A_M_lmer_Alt_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 388.62 388.62 1 6.4921 3.5587 0.1045
anova(DT_A_F_lmer_Alt_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 130.78 130.78 1 0.88974 0.4161 0.6457
anova(DT_A_M_lmer_Alt_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 333.57 333.57 1 0.62544 1.1083 0.5539
anova(DT_A_F_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 13.445 13.445 1 6.6617 0.0867 0.7774
anova(DT_A_M_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 7.4907 7.4907 1 6.6463 0.0361 0.8549
anova(DT_A_F_lmer_Alt_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 11.642 11.642 1 7.0463 0.0537 0.8233
DT_A_M_lmer_Alt_Schmidt <- lmer(DT_EggAdult ~ Altitude + (1|Population) + (1|Line:Population), data = d_DT_A_M[d_DT_A_M$Supervisor.PI == "Schmidt",])
capture.output(summary(DT_A_M_lmer_Alt_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Alt_Schmidt_sum.txt")
capture.output(anova(DT_A_M_lmer_Alt_Schmidt),file = "DevelopmentTime/DT_A_M_lmer_Alt_Schmidt.txt")
anova(DT_A_M_lmer_Alt_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 28.785 28.785 1 6.7078 0.0969 0.7651
anova(DT_A_F_lmer_Alt_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 6.1328 6.1328 1 6.9047 0.0153 0.905
anova(DT_A_M_lmer_Alt_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 10.826 10.826 1 6.9162 0.0254 0.878
anova(DT_A_F_lmer_Alt_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 42.459 42.459 1 6.6749 0.3623 0.5671
anova(DT_A_M_lmer_Alt_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 217.23 217.23 1 6.4051 1.6952 0.2378
anova(DW_F_lmer_Alt_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.00058901 0.00058901 1 6.5839 0.2186 0.6552
anova(DW_M_lmer_Alt_Colinet)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.0010067 0.0010067 1 6.8721 1.4059 0.2751
anova(DW_F_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.0019488 0.0019488 1 6.8061 0.8625 0.3848
## boundary (singular) fit: see ?isSingular
anova(DW_M_lmer_Alt_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.00079782 0.00079782 1 164.91 1.1641 0.2822
anova(DW_F_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.00039668 0.00039668 1 6.9485 0.1492 0.7109
DW_M_lmer_Alt_Onder <- lmer(DW_micrograms ~ Altitude + (1|Population) + (1|Population:Line),data = d_DW_M[d_DW_M$Supervisor.PI == "Onder",])
capture.output(summary(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder_sum.txt")
capture.output(anova(DW_M_lmer_Alt_Onder),file = "DryWeight/DW_M_lmer_Alt_Onder.txt")
anova(DW_M_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 3.1127e-05 3.1127e-05 1 6.6562 0.0339 0.8594
anova(TL_F_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 7428.9 7428.9 1 6.6794 2.9103 0.1338
anova(TL_M_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 4631.5 4631.5 1 6.8648 2.0583 0.1953
anova(TL_F_lmer_Alt_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 186.88 186.88 1 7 0.1216 0.7376
anova(TL_M_lmer_Alt_Posnien)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 841.27 841.27 1 7.0004 0.6923 0.4328
## boundary (singular) fit: see ?isSingular
anova(TL_F_lmer_Alt_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 8529.6 8529.6 1 23.916 3.0285 0.09467 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## boundary (singular) fit: see ?isSingular
anova(TL_M_lmer_Alt_Ritchie)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 8644.9 8644.9 1 24.071 2.4152 0.1332
anova(TL_F_lmer_Alt_Schmidt)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.097583 0.097583 1 6.439 1e-04 0.9932
dir.create(file.path("WingArea"), showWarnings = FALSE)
anova(WA_Alt_F_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 342.29 342.29 1 6.8913 0.053 0.8245
anova(WA_Alt_M_L_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 800.11 800.11 1 6.8758 0.1522 0.7083
anova(WA_Alt_F_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 427.13 427.13 1 7 0.0512 0.8275
## boundary (singular) fit: see ?isSingular
anova(WA_Alt_M_L_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 131.41 131.41 1 25 0.0219 0.8835
anova(WA_Alt_F_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 5599 5599 1 7.9061 0.4321 0.5296
anova(WA_Alt_M_L_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 99.14 99.14 1 7.1968 0.0101 0.9227
anova(WA_Alt_F_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 500.23 500.23 1 7.0147 0.0646 0.8066
anova(WA_Alt_M_L_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 453.74 453.74 1 7.0222 0.0758 0.7909
anova(WA_Alt_F_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 429.92 429.92 1 6.8921 0.0665 0.804
anova(WA_Alt_M_R_Onder_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 975.76 975.76 1 6.8726 0.1835 0.6815
anova(WA_Alt_F_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 306.55 306.55 1 6.9999 0.0353 0.8564
## boundary (singular) fit: see ?isSingular
anova(WA_Alt_M_R_Posnien_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 30.438 30.438 1 25 0.005 0.9439
anova(WA_Alt_F_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 6054.3 6054.3 1 7.8555 0.461 0.5167
anova(WA_Alt_M_R_Ritchie_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 48.386 48.386 1 7.1818 0.005 0.9456
anova(WA_Alt_F_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 637.13 637.13 1 7.0173 0.0827 0.782
anova(WA_Alt_M_R_StamenkovicRadak_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 439.77 439.77 1 7.0227 0.0749 0.7922
dir.create(file.path("Fecundity"), showWarnings = FALSE)
anova(Fec_lmer_Alt_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 2814.1 2814.1 1 6.3615 1.0185 0.3497
## boundary (singular) fit: see ?isSingular
anova(Fec_lmer_Alt_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 134.07 134.07 1 157.2 0.1062 0.7449
dir.create(file.path("Lifespan"), showWarnings = FALSE)
anova(LS_Alt_P_F_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 13.92 13.92 1 7.0003 0.0861 0.7777
anova(LS_Alt_P_M_Flatt_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 20.883 20.883 1 6.9979 0.1172 0.7422
anova(LS_Alt_L_F_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 199.34 199.34 1 7.0096 0.6394 0.4502
anova(LS_Alt_L_M_Parsch_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 287.14 287.14 1 7.007 0.781 0.4061
anova(LS_Alt_L_F_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 344.59 344.59 1 6.6393 1.3987 0.2776
anova(LS_Alt_L_M_Pasyukova_lmer)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 664.65 664.65 1 6.8038 2.0548 0.196
anova(CSM_F_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.015338 0.015338 1 6.6251 0.3392 0.5796
anova(CSM_M_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.058672 0.058672 1 6.2882 0.6623 0.4455
## boundary (singular) fit: see ?isSingular
anova(CSM_F_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.042761 0.042761 1 125.6 0.3235 0.5706
## boundary (singular) fit: see ?isSingular
anova(CSM_M_lmer_Alt_Kozeretska)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.0021266 0.0021266 1 126.38 0.0152 0.9021
anova(CSM_F_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.00015353 0.00015353 1 6.8118 0.0051 0.9451
anova(CSM_M_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.16878 0.16878 1 7.0405 4.5354 0.07048 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(CCRT_F_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 36990 36990 1 6.9544 0.2017 0.667
anova(CCRT_M_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 186.03 186.03 1 6.6486 0.0012 0.9736
dir.create(file.path("HeatShock"), showWarnings = FALSE)
For a detailed description of tables, plots, linear models and outputs, please refer here
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0031852 (tol = 0.002, component 1)
anova(HSM_F_lmer_Alt_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 9281.8 9281.8 1 6.9945 1.4148 0.2731
anova(HSM_M_lmer_Alt_Parsch)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 9366.2 9366.2 1 7.002 1.762 0.226
anova(HSM_F_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 193.44 193.44 1 6.8578 0.0635 0.8085
anova(HSM_M_lmer_Alt_Vieira)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 488.98 488.98 1 6.3519 0.1641 0.6987
anova(Dia_lmer_Alt_Bergland)
anova(Dia_lmer_Alt_Flatt)
anova(Dia_lmer_Alt_Schlotterer)
ND
anova(LA_lmer_Alt_ND_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.26489 0.26489 1 6.2455 1.7913 0.2274
CircPhase
anova(LA_lmer_Alt_CircPhase_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 7.3304 7.3304 1 6.7108 0.1571 0.7041
Activity
anova(LA_lmer_Alt_Activity_Tauber)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.31643 0.31643 1 6.4338 0.0044 0.9492
anova(SR_F_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 309.49 309.49 1 6.9347 0.7251 0.4229
anova(SR_M_lmer_Alt_Gonzalez)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 227.32 227.32 1 6.9278 1.0456 0.3409
anova(SR_F_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 189.75 189.75 1 6.9417 0.5884 0.4683
anova(SR_M_lmer_Alt_Onder)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 104.15 104.15 1 6.8734 0.5309 0.4903
anova(SR_F_lmer_Alt_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 2203.2 2203.2 1 6.9956 4.5833 0.06957 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SR_M_lmer_Alt_Pasyukova)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 436.78 436.78 1 6.9885 1.5442 0.2541
dir.create(file.path("Pigmentation"), showWarnings = FALSE)
PercT4
anova(Pgm_lmer_Alt_Abbott_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 80.513 80.513 1 6.9765 2.6051 0.1507
PercT5
anova(Pgm_lmer_Alt_Abbott_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 74.957 74.957 1 6.9966 2.1662 0.1846
PercT6
anova(Pgm_lmer_Alt_Abbott_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 14.089 14.089 1 6.8446 0.088 0.7755
TotalPerc
Pgm_lmer_Alt_Abbott_TotalPerc <- lmer(TotalPerc ~ Altitude + (1|Population) + (1|Line:Population) ,data = (subset(d_Pgm,Supervisor.PI=='Abbott')))
capture.output(summary(Pgm_lmer_Alt_Abbott_TotalPerc),file = "Pigmentation/Pgm_lmer_Alt_Abbott_TotalPerc_sum.txt")
capture.output(anova(Pgm_lmer_Alt_Abbott_TotalPerc),file = "Pigmentation/Pgm_lmer_Alt_Abbott_TotalPerc.txt")
anova(Pgm_lmer_Alt_Abbott_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 43.887 43.887 1 6.9567 1.2827 0.2949
PercT4
anova(Pgm_lmer_Alt_Gibert_PercT4)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 350.43 350.43 1 6.3555 3.8698 0.09404 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
PercT5
anova(Pgm_lmer_Alt_Gibert_PercT5)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 979.66 979.66 1 6.3498 9.2806 0.02107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
PercT6
anova(Pgm_lmer_Alt_Gibert_PercT6)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 16.984 16.984 1 6.599 0.0817 0.7837
TotalPerc
## boundary (singular) fit: see ?isSingular
anova(Pgm_lmer_Alt_Gibert_TotalPerc)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 195.69 195.69 1 166.39 2.6614 0.1047
Tergite8
anova(Pgm2_lmer_Alt_Schmidt_Tergite8)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.34116 0.34116 1 6.5819 0.4746 0.5144
Tergite9
anova(Pgm2_lmer_Alt_Schmidt_Tergite9)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.045283 0.045283 1 5.4536 0.0178 0.8987
Tergite10
anova(Pgm2_lmer_Alt_Schmidt_Tergite10)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 0.83686 0.83686 1 6.2094 0.1982 0.6713
Total
anova(Pgm2_lmer_Alt_Schmidt_Total)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Altitude 2.6253 2.6253 1 5.9721 0.1934 0.6755
@ Schmidt lab, females were allowed to lay eggs for Xh, and viability is calculated as the percentage of individuals that emerged from total number of eggs, per vial.↩︎